06: AutoViML_Python.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "AutoViML_Python.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/gulabpatel/AutoML/blob/main/AutoViML_Python.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RYcZgsYbgIE7"
      },
      "source": [
        "autoviml Auto Variant Interpretation Machine learning\n",
        "\n",
        "Autoviml does feature engeering to some extent."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kbHRfwu_CTJP",
        "outputId": "af705130-e6a1-4ee4-cc9f-e4e975ef2fb7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "!pip install autoviml"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kNEGwU3oU00R",
        "outputId": "69913522-374f-44b4-9112-380b74e04db1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 292
        }
      },
      "source": [
        "!pip install shap"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wYsohgWlDB8X"
      },
      "source": [
        "from __future__ import print_function\n",
        "import sys,tempfile, urllib, os\n",
        "import pandas as pd"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "elkIXKcHC06h"
      },
      "source": [
        "#BASE_DIR = '/tmp'\n",
        "#OUTPUT_FILE = os.path.join(BASE_DIR, 'churn_data.csv')\n",
        "#churn_data=urllib.request.urlretrieve('https://raw.githubusercontent.com/srivatsan88/YouTubeLI/master/dataset/WA_Fn-UseC_-Telco-Customer-Churn.csv', OUTPUT_FILE)"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "esk0QhacDQEj"
      },
      "source": [
        "churn_df = pd.read_csv('/content/Churn.csv')"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cwaOT3fCDVR8",
        "outputId": "665cc1f3-df6d-466d-f62f-3fcd38965c2c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309
        }
      },
      "source": [
        "churn_df.head()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Gender</th>\n",
              "      <th>Senior_Citizen</th>\n",
              "      <th>Partner</th>\n",
              "      <th>Dependents</th>\n",
              "      <th>Tenure_Months</th>\n",
              "      <th>Phone_Service</th>\n",
              "      <th>Multiple_Lines</th>\n",
              "      <th>Internet_Service</th>\n",
              "      <th>Online_Security</th>\n",
              "      <th>Online_Backup</th>\n",
              "      <th>Device_Protection</th>\n",
              "      <th>Tech_Support</th>\n",
              "      <th>Streaming_TV</th>\n",
              "      <th>Streaming_Movies</th>\n",
              "      <th>Contract</th>\n",
              "      <th>Paperless_Billing</th>\n",
              "      <th>Payment_Method</th>\n",
              "      <th>Monthly_Charges</th>\n",
              "      <th>Total_Charges</th>\n",
              "      <th>Churn_Label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>2</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>DSL</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Month-to-month</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Mailed check</td>\n",
              "      <td>53.85</td>\n",
              "      <td>108.15</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Female</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Fiber optic</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Month-to-month</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Electronic check</td>\n",
              "      <td>70.70</td>\n",
              "      <td>151.65</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Female</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>8</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Fiber optic</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Month-to-month</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Electronic check</td>\n",
              "      <td>99.65</td>\n",
              "      <td>820.5</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Female</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>28</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Fiber optic</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Month-to-month</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Electronic check</td>\n",
              "      <td>104.80</td>\n",
              "      <td>3046.05</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>49</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Fiber optic</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Month-to-month</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Bank transfer (automatic)</td>\n",
              "      <td>103.70</td>\n",
              "      <td>5036.3</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Gender Senior_Citizen Partner  ... Monthly_Charges  Total_Charges Churn_Label\n",
              "0    Male             No      No  ...           53.85         108.15         Yes\n",
              "1  Female             No      No  ...           70.70         151.65         Yes\n",
              "2  Female             No      No  ...           99.65          820.5         Yes\n",
              "3  Female             No     Yes  ...          104.80        3046.05         Yes\n",
              "4    Male             No      No  ...          103.70         5036.3         Yes\n",
              "\n",
              "[5 rows x 20 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RAkXBnJkDYoB"
      },
      "source": [
        "size = int(0.7*churn_df.shape[0])\n",
        "train_df = churn_df[:size]\n",
        "test_df = churn_df[size:]"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IJEeNCNQEn2c",
        "outputId": "67395d53-3fde-441e-8d97-f015381d8a74",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "from autoviml.Auto_ViML import Auto_ViML"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading collection 'popular'\n",
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          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<style>.container { width:95% !important; }</style>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Imported Auto_NLP version: 0.0.44.. Call using:\n",
            "     train_nlp, test_nlp, nlp_pipeline, predictions = Auto_NLP(\n",
            "                nlp_column, train, test, target, score_type='balanced_accuracy',\n",
            "                modeltype='Classification',top_num_features=200, verbose=0,\n",
            "                build_model=True)\n",
            "Imported Auto_ViML version: 0.1.665. Call using:\n",
            "             m, feats, trainm, testm = Auto_ViML(train, target, test,\n",
            "                            sample_submission='',\n",
            "                            scoring_parameter='', KMeans_Featurizer=False,\n",
            "                            hyper_param='RS',feature_reduction=True,\n",
            "                             Boosting_Flag='CatBoost', Binning_Flag=False,\n",
            "                            Add_Poly=0, Stacking_Flag=False,Imbalanced_Flag=False,\n",
            "                            verbose=1)\n",
            "            \n",
            "To remove previous versions, perform 'pip uninstall autoviml'\n",
            "NEW! Now Auto_ViML comes with a feature_engineering module using featuretools library!\n",
            "To get the latest version, perform \"pip install autoviml --no-cache-dir --ignore-installed\"\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T3tKIdKGFUpQ"
      },
      "source": [
        "target='Churn_Label'"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CT3wE4N7FNvf",
        "outputId": "8f6657fd-1ff4-4b02-fec3-e21983fe2ac5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "model, features, trainm, testm = Auto_ViML(train_df, target, test_df, sample_submission='',\n",
        "                                    scoring_parameter='',\n",
        "                                    hyper_param='RS',feature_reduction=True,\n",
        "                                     Boosting_Flag=True,Binning_Flag=True,\n",
        "                                    Add_Poly=0, Stacking_Flag=False, \n",
        "                                    Imbalanced_Flag=True, \n",
        "                                    verbose=1)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "##############  D A T A   S E T  A N A L Y S I S  #######################\n",
            "Training Set Shape = (4930, 20)\n",
            "    Training Set Memory Usage = 0.75 MB\n",
            "Test Set Shape = (2113, 20)\n",
            "    Test Set Memory Usage = 0.32 MB\n",
            "Single_Label Target: ['Churn_Label']\n",
            "Shuffling the data set before training\n",
            "       Class  -> Counts -> Percent\n",
            "          No:    3061  ->   62.1%\n",
            "         Yes:    1869  ->   37.9%\n",
            "Using RandomizedSearchCV for Hyper Parameter Tuning. This is 3X faster than GridSearchCV...\n",
            "String or Multi Class target: Churn_Label transformed as follows: {'No': 0, 'Yes': 1}\n",
            "############## C L A S S I F Y I N G  V A R I A B L E S  ####################\n",
            "Classifying variables in data set...\n",
            "    Number of Numeric Columns =  1\n",
            "    Number of Integer-Categorical Columns =  1\n",
            "    Number of String-Categorical Columns =  10\n",
            "    Number of Factor-Categorical Columns =  0\n",
            "    Number of String-Boolean Columns =  6\n",
            "    Number of Numeric-Boolean Columns =  0\n",
            "    Number of Discrete String Columns =  1\n",
            "    Number of NLP String Columns =  0\n",
            "    Number of Date Time Columns =  0\n",
            "    Number of ID Columns =  0\n",
            "    Number of Columns to Delete =  0\n",
            "    19 Predictors classified...\n",
            "        This does not include the Target column(s)\n",
            "    No variables removed since no ID or low-information variables found in data set\n",
            "Number of GPUs = 2\n",
            "GPU available on this device\n",
            "#############     D A T A    P R E P A R A T I O N   AND C L E A N I N G     #############\n",
            "No Missing Values in train data set\n",
            "Test data has no missing values. Continuing...\n",
            "Completed Label Encoding and Filling of Missing Values for Train and Test Data\n",
            "Binary_Classification problem: hyperparameters are being optimized for balanced_accuracy\n",
            "Searching for and removing highly correlated variables amoung 2 numeric variables\n",
            "    No numeric vars removed since none have high correlation with each other in this data...\n",
            "\n",
            "############# PROCESSING T A R G E T = Churn_Label ##########################\n",
            "Selecting only (1) float variables this time to remove correlated features...\n",
            "No categorical feature reduction done. All 18 Categorical vars selected \n",
            "    Adding 18 categorical variables to reduced numeric variables  of 1\n",
            "############## F E A T U R E   S E L E C T I O N  ####################\n",
            "Current number of predictors = 19 \n",
            "    Finding Important Features using Boosted Trees algorithm...\n",
            "        using 19 variables...\n",
            "        using 15 variables...\n",
            "        using 11 variables...\n",
            "        using 7 variables...\n",
            "        using 3 variables...\n",
            "Found 13 important features\n",
            "Starting Feature Engineering now...\n",
            "Train CV Split completed with TRAIN rows: 4225 CV rows: 705\n",
            "    Binning_Flag set to False or there are no numeric vars in data set to be binned\n",
            "    KMeans_Featurizer set to False or there are no numeric vars in data\n",
            "Feature scaling for total 13 float and integer variables completed using MinMaxScaler()...\n",
            "###############  M O D E L   B U I L D I N G  B E G I N S  ####################\n",
            "Rows in Train data set = 4225\n",
            "  Features in Train data set = 13\n",
            "    Rows in held-out data set = 705\n",
            "Finding Best Model and Hyper Parameters for Target: Churn_Label...\n",
            "    Baseline Accuracy Needed for Model = 62.09%\n",
            "CPU Count = 2 in this device\n",
            "Using XGBoost Model, Estimated Training time = 1.37 mins\n",
            "##############  Imbalanced Flag on: Training model with SMOTE Oversampling method  ###########\n",
            "    Number of Rare Class samples = 1589\n",
            "    Number of K Neighbors selected for SMOTE = 10\n",
            "    Pct of Rare Class in data = 37.61%\n",
            "This is not an Imbalanced data set. No need to use SMOTE but continuing...\n",
            "    SMOTE completed. Actual time taken = 0 seconds\n",
            "##################  Training RandomizedSearchCV on Imbalanced data...  ################\n",
            "Error: GPU exists but it is not turned on. Using CPU for predictions...\n",
            "Imbalanced class training completed.\n",
            "    Actual training time (in seconds): 131\n",
            "###########  S I N G L E  M O D E L   R E S U L T S #################\n",
            "7-fold Cross Validation logloss = -6.5514270871056555\n",
            "    Best Parameters for Model = {'gamma': 10, 'learning_rate': 0.6939140133043014, 'max_depth': 1, 'n_estimators': 231}\n",
            "Best Model selected and its parameters are:\n",
            "    XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
            "              colsample_bynode=1, colsample_bytree=0.7, gamma=10, gpu_id=0,\n",
            "              grow_policy='depthwise', importance_type='gain',\n",
            "              interaction_constraints='', learning_rate=0.6939140133043014,\n",
            "              max_delta_step=0, max_depth=1, max_leaves=0, min_child_weight=1,\n",
            "              missing=-999, monotone_constraints='()', n_estimators=231,\n",
            "              n_jobs=-1, nthread=-1, num_parallel_tree=1,\n",
            "              predictor='cpu_predictor', random_state=1, reg_alpha=0.5,\n",
            "              reg_lambda=0.5, scale_pos_weight=1, seed=1, subsample=0.7,\n",
            "              tree_method='hist', updater='grow_colmaker', ...)\n",
            "Finding Best Threshold for Highest F1 Score...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "    Using threshold=0.5. However, 0.546 provides better F1=0.73 for rare class...\n",
            "########################################################\n",
            "XGBoost Model Prediction Results on Held Out CV Data Set:\n",
            "    Regular Accuracy Score = 78.3%\n",
            "Balanced Accuracy = 76.14% with Threshold = 0.50\n",
            "Confusion Matrix:\n",
            "[[333  92]\n",
            " [ 73 207]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.82      0.78      0.80       425\n",
            "           1       0.69      0.74      0.72       280\n",
            "\n",
            "    accuracy                           0.77       705\n",
            "   macro avg       0.76      0.76      0.76       705\n",
            "weighted avg       0.77      0.77      0.77       705\n",
            "\n",
            "Balanced Accuracy = 77.37% with Threshold = 0.55\n",
            "Confusion Matrix:\n",
            "[[351  74]\n",
            " [ 78 202]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.82      0.83      0.82       425\n",
            "           1       0.73      0.72      0.73       280\n",
            "\n",
            "    accuracy                           0.78       705\n",
            "   macro avg       0.78      0.77      0.77       705\n",
            "weighted avg       0.78      0.78      0.78       705\n",
            "\n",
            "#####################################################################\n",
            "Modified threshold = 0.55 is better\n",
            "################# E N S E M B L E  M O D E L  ##################\n",
            "Time taken = 2 seconds\n",
            "Based on trying multiple models, Best type of algorithm for this data set is Bagging_Classifier\n",
            "    Displaying results of weighted average ensemble of 5 classifiers\n",
            "#############################################################################\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<style>.container { width:95% !important; }</style>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "After multiple models, Ensemble Model Results:\n",
            "    Balanced Accuracy Score = 77.187%\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.82      0.83      0.82       425\n",
            "           1       0.73      0.72      0.72       280\n",
            "\n",
            "    accuracy                           0.78       705\n",
            "   macro avg       0.77      0.77      0.77       705\n",
            "weighted avg       0.78      0.78      0.78       705\n",
            "\n",
            "[[351  74]\n",
            " [ 79 201]]\n",
            "#############################################################################\n",
            "Single Model is better than Ensembling Models for this data set.\n",
            "Could not plot Model Evaluation Results Metrics\n",
            "    Time taken for this Target (in seconds) = 137\n",
            "Training model on complete Train data and Predicting using give Test Data...\n",
            "    Binning_Flag set to False or there are no numeric vars in data set to be binned\n",
            "Feature scaling for total 13 float and integer variables completed using MinMaxScaler()...\n",
            "##################  Imbalanced Flag Set  ############################\n",
            "Imbalanced Class Training using SMOTE Rare Class Oversampling method...\n",
            "    Number of Rare Class samples = 1869\n",
            "    Number of K Neighbors selected for SMOTE = 10\n",
            "    Pct of Rare Class in data = 37.91%\n",
            "This is not an Imbalanced data set. No need to use SMOTE but continuing...\n",
            "    SMOTE completed. Actual time taken = 0 seconds\n",
            "##################  Training XGBClassifier on Imbalanced data...  ################\n",
            "Imbalanced class training completed.\n",
            "Actual Training time taken in seconds = 1\n",
            "    Making test Data predictions using modified Threshold = 0.546\n",
            "    Calculating weighted average ensemble of 5 classifiers\n",
            "########################################################\n",
            "Completed Ensemble predictions on held out data\n",
            "Plotting Feature Importances to explain the output of model\n",
            "############### P R E D I C T I O N  O N  T E S T   C O M P L E T E D  #################\n",
            "    Time taken for this Target (in seconds) = 139\n",
            "Writing Output files to disk...\n",
            "    Saving predictions to ./Churn_Label/Churn_Label_Binary_Classification_test_modified.csv\n",
            "    Saving predictions to ./Churn_Label/Churn_Label_Binary_Classification_submission.csv\n",
            "    Saving predictions to ./Churn_Label/Churn_Label_Binary_Classification_train_modified.csv\n",
            "###############  C O M P L E T E D  ################\n",
            "Time Taken in mins = 2.3 for the Entire Process\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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bt++u6sydO7emTp2q3r17q3///po8eXKaMasZyZs3r2W6usKFC6t27dp3daxHUfHixdW1a1d17dpV165dU69evTRjxgz17NlTBQoUUIkSJXTs2DFVqVLlgYzpz4rDhw+ratWq6ZZZW1urSJEi2Xbc/Pnzy9HRUUePHk23LqNld8va2lqtWrXS9OnTlStXrtv+R1A3e/APHjyYrlf40KFDadrcnOv6yJEj6doePnw4zeP8+fMrb968io+Pv+tvWjLi5OSkoKAgyxSEN282Xbhw4T3d5Ori4qKBAwdq5MiRmjVrlvr27StJKlGihM6fPy8/P7+76lm+m7pcXFwy/CbmbmaSkW7fYXBz/y1btlTLli1lNpv1wQcfaMaMGVq9enWGY/jxeOJ7EACGa9eunUqWLKlZs2Zpw4YN6dbv3btXH330kQoVKpRmHHHJkiUlyTIe9qbly5crJibmjsctX768PDw89MMPP+jgwYPp1mfU+5k7d259+eWXlq/Ob84Eczsvvvii+vfvr1GjRuXIr58vX76cbho6e3t7y1CHmx+GWrVqpdTUVH300UcZ7ufs2bPZVuOMGTPSvF579+7V5s2bVbNmzWz9YGBtba06depoz5492rFjR5p1t864ci86duyo/v37a8yYMWnGgv9X7dq15ejoqNmzZ6cZ7hUfH6/Zs2fL0dHR8iGwdu3aypUrl+bMmaPExERL2+joaC1btizNfk0mk4KDg7Vnz55M/2OkO/3PuBkNx6pYsaKkjD9E362WLVuqePHi+uqrryzn3KpVK8XGxmrmzJkZbnPr79/d1lWyZEnt3r07zXN18eLFdFNPZsbR0THdPqUbPeuXLl1Ks8zKysoyZO5+nhvkPPSMAzCcg4ODpce5T58+aty4sXx8fGRjY6M9e/YoLCxMzs7Omjp1qgoWLGjZrnTp0qpVq5YWLFggs9msZ555Rvv27VN4eLhKlChxx6EqVlZWGjt2rHr06KH27dtbpja8dOmStm/frjp16mT43447OTnpiy++UN++fTV48GBNnDgx3Y1+typfvnyWpjKMi4vTZ599luG6tm3bWqa7y8yxY8cUFhaW4bpatWqpUKFCd12LdOPGzVGjRqlx48YqVaqUnJyc9Mcff2jhwoWqUqWKJZQ3adJEbdq00ezZs7V3717LXOvR0dHavXu3jh8/rtWrV2fp2HcrKipKvXr1UmBgoGJjYzV79mzlypXLMoQmOw0cOFAbN25U79691aVLF7m7u2vdunWWwHen3tHMFClSRK+++uod2+XNm1dDhgxRSEiIOnToYJlqcvHixTp+/LhCQkIsN+E6OztrwIABGj9+vDp27KhWrVopMTFR8+fPV8mSJdPNxz1o0CDt3LlTAwcOVNOmTVWlShXZ2toqKipKGzZsUMWKFW87m0qzZs1UtWpVVa5cWa6uroqNjdV3330nW1tbNW/e/J6eF+nGNJ19+vTRW2+9pa+//lr9+vVTt27dtHnzZk2YMEFbt26Vn5+fcufOraioKG3dulV2dnaW2VTutq7OnTvrjTfeUPfu3dWyZUtdunRJ33//vYoUKZLhfPP/ValSJZlMJk2bNk0XL16Uo6OjihUrplKlSsnf31+BgYGqUKGC8ufPr5MnT2revHlydnZWQEDAPT83yHkI4wAeCaVLl9bSpUv19ddfa9WqVdqwYYOuXLki6cY447lz56abI1uSJkyYoHfeeUfLli3T0qVLVb16dX3zzTd6++23M5x1478qV66shQsX6rPPPtOKFSs0f/58ubi4qHLlyqpWrVqm2zk6Ourzzz9Xv379NGTIEKWkpKQZQnM/zp07p48//jjDdbVq1bpjGN+0aZM2bdqU4bqZM2dmOYyXK1dOjRo10rZt27Rs2TKlpqaqcOHC6tOnT7oZQ95//335+vrqu+++0/Tp05WcnKxChQqpQoUKev3117N03KyYMWOG3n//fYWGhurq1auqUqWKhg4dmq3zud9UunRpzZkzR+PHj9c333wje3t71a9fX6NHj1bDhg0tc8xnp5s3In755ZeaMmWKpBsfAqdMmZJuKFXPnj3l6OiomTNn6sMPP1ThwoXVs2dP5cmTRyNHjkzTNk+ePJo3b56++uor/fzzz1q9erWsra3l7u6u6tWrq3379retq2fPnlq/fr2+/fZbXb58WQUKFFCVKlXUp0+f+35tWrVqpc8++0yzZs1St27dlCdPHk2fPl1z585VWFiYQkNDJd2YFadSpUpp5sO/27patGihmJgYzZkzR++//76KFy+uV155RSaTSb/99tsdayxSpIjGjh2rL774QmPGjFFycrJat26tkJAQde/eXVu2bLHM+OPq6qrAwED16dPnjtc4Hi9W5od1JwwAZFFKSooGDBig8PBwjRgxQj169DC6JOCu/fHHH2rbtq1ef/11vfTSS0aXA+ARlfMGLwJ4YtjY2GjSpEmqV6+e3n//fc2dO9fokoAM3Zy/+iaz2awZM2ZI0n3d/Ajg8UfPOAAA9ykoKEh+fn4qW7asEhMTtXbtWkVGRqpZs2aaNGmS0eUBeIQRxgEAuE8TJkzQ2rVrFR0drZSUFBUrVkzBwcF68cUX7zjFJoAnG2EcAAAAMAizqSBHSj57xOgScI+u799idAm4D9fX3XledTya7Pq8bXQJuA/JCzKevx85g2O/TzNdxw2cAAAAgEEI4wAAAIBBCOMAAACAQQjjAAAAgEEI4wAAAIBBCOMAAACAQQjjAAAAgEEI4wAAAIBBCOMAAACAQQjjAAAAgEEI4wAAAIBBCOMAAACAQQjjAAAAgEEI4wAAAIBBCOMAAACAQQjjAAAAgEEI4wAAAIBBCOMAAACAQQjjAAAAgEEI4wAAAIBBCOMAAACAQQjjAAAAgEEI4wAAAIBBCOMAAACAQQjjAAAAgEEI4wAAAIBBCOMAAACAQQjjAAAAgEEI4wAAAIBBCOMAAACAQQjjAAAAgEEI4wAAAIBBCOMAAACAQQjjAAAAgEEI4wAAAIBBCOMAAACAQQjjAAAAgEEI4wAAAIBBCOMAAACAQQjjAAAAgEEI4wAAAIBBCOMAAACAQQjjAAAAgEEI4wAAAIBBCOMAAACAQQjjAAAAgEEI4wAAAIBBCOMAAACAQQjjAAAAgEEI4wAAAIBBCOMAAACAQQjjAAAAgEEI4wAAAIBBCOMAAACAQQjjAAAAgEEI4wAAAIBBCOMAAACAQQjjAAAAgEEI4wAAAIBBCOMAAACAQQjjAAAAgEEI4wAAAIBBCOMAAACAQWyMLuBx07x5c40ePVq+vr6G1RAYGKh3331XtWrVeuD7joyM1JtvvqmVK1dKko4cOaJBgwbpxIkTGjRokA4dOiQ3Nzf169fvgR/7cbBxa6TGTZ6m66mpahvcRL27dkizfvzH07Vt5x5J0tVr1xR3/oK2rFyobTt+0/hPPre0O3rib00cM1wN6j741xiZ2/T7IY2fu1Kp5lS1ruOlXs3906yfOG+ltv91TJKUmJSs85cStHHKMG3bd1QfzP/F0u7o6bMa37etAquVf5jlP9GsS1eSXeMukpVJKbvXK3nL8jTr7Ro+L1PJZyRJVjb2snLKoysfvnzjcd4Csm/eU1Z5C0hms64u+FDmi2cf+jk8yXjvzNk2HTuriRv2K9VsVquKRdXTu1Sa9R9s2K/tJ+MkSVdTUhV3JUm/9g2QJJ2+nKiQ8D91Jv6aJOnTll4qktfh4Z5ANntsw3hgYKDOnj0ra2tr2djYyMvLS2PGjFHhwoWz9bg//vhjtu5fkuLj4/Xxxx9r1apVunjxogoUKKCAgAC9/PLLyp8/f7Ye29vb2xLEJWnGjBny9fVVWFhYth73cXD9+nW9++EUfTF5rNxdC+q53gMU4O+rMqVKWNoMG9DH8vOc78O07+BhSZJP9Sr64espkqSLly6raYeequVT7eGewBPuemqqxs5eoemvd5Fb/rx6PmSG6lctpzJFC1navNEpyPLz3PBt+utEtCTJ55lS+m7Mjdf2Ynyinh0RqpoVyzzcE3iSWVnJrkk3XZ07QeZLccrVc4xSDu6U+WyUpUlS+FzLzzbejWRy//e6tG/xkpI2LVXq0b2Srb1kNj/U8p90vHfmbNdTzRq37i9NbV1NbrlzqfOCCNUrVUhlCuS2tBlSt5zl53m/ndD+2MuWx6N+2aveNUrJ76kCupKUIisrq4da/8PwWA9TmTZtmnbt2qWNGzeqQIECeuedd4wu6b4lJSWpe/fuOnTokGbMmKEdO3ZowYIFcnFx0e+///7Q64mKipKHh8d97yclJeUBVPNo+33fAT1VrIiKFy0sW1tbNW1QT2t+3Zpp+5/C16tZw/rplv+y9lfV8fOWQ65c2Vgt/uuPI6dU3DWfirnmk62NtZr4VtS63fszbf9zxB9q6lsx3fJVkX/Kv9LTcrC3zc5ycQtTkTJKjYuR+UKslHpd1//cKpuymQcym4p+Stm7RZJkVbCIZLK+EcQlKfmalJL0MMrGP3jvzNn+OHNRxV0cVczZUbbWJgV5uGvdkdhM2/+8P1pNyrpLkg6fi9f1VLP8niogSXK0s5GDrfVDqftheqzD+E329vZq0qSJDh++8Ul53bp1atWqlapVq6Z69eopNDQ0TfslS5YoICBAvr6+mjJligIDA7V582ZJ0tWrVzVs2DDVqFFDTZs21RdffKG6detatr21bWhoqAYMGKChQ4fKy8tLzZs3TxOY9+7dq1atWsnLy0uvvfaaBg4cqEmTJt32XMLCwnT69Gl9+umnevrpp2UymVSgQAH169dP9erVS9d+z549eu655+Tt7S1/f3+FhIQoKenGHxKz2ayxY8eqZs2aqlatmoKDg3XgwAFJ0vr169WsWTN5eXmpTp06+vLLLyVJERERlvPt1q2bIiIiFBISIi8vLx09elTDhw9Pcw5r165Vy5Yt5e3trY4dO+qvv/5K81x9/vnnCg4OVtWqVR/7QB4Te1burv/2orq5FlRM7LkM20ZFn9Gp09HyrV4l3boV4RvUtFH97CoTmYi5cFnu+Z0tj13z5dWZ85czbBt19oJOnb0gn2dKpVv387a9auLjmW11Ij2rPPlkvvzvtWa+FCerPPkybpu3gKxcCin12J+SJFN+d5mvXpF929eUq9c7sg3sKD2GPXOPMt47c7aY+Gtyy21veeyW216xCdcybBt1KVFRlxJVo9iNb/lPXLiiPPY2ev3H39Rx7lZN2nhA11Mfv2+mnogwnpiYqJ9++klVqty4OB0cHDR+/HhFRkZq+vTpmjdvnsLDwyVJhw4d0pgxYzRx4kT9+uuvio+P15kzZyz7+vTTT3Xq1CmFh4dr5syZWrp06W2PvWbNGjVv3lyRkZEKDAy09M4nJSWpf//+at26tbZt26Znn33WUsPtbN68WXXq1JGTk9NdnbvJZNKIESO0detWzZ8/X1u2bNHcuTe+jt24caMiIyO1cuVK7dixQ5MnT5aLi4sk6c0331RISIh27dql5cuXy8/PL92+v/nmG3l7e2v06NHatWuXSpVKGzz+/PNPjRw5UiEhIYqIiNBzzz2nV155xfJhQLoxrOfzzz9XZGSkbGwe21FTWbYifL0a1/eXtXXaHoDYs3E6eOSoavtWN6gy3I2ft+1VQ+9nZG1K+xYbe+GyDp2MUS1Phqg8qmwq+un6vu3/DkUxWcu6eFklrZ6nq1/9T6Z8hWRTuY6xRSJTvHfmbCsPRKvB026yNt34wJuSatauqAsa5O+h2R19dPJiopbui7rDXnKexzqM9+vXT97e3vL29tamTZvUq1cvSZKvr6/KlSsnk8mk8uXLq3nz5tq2bZsk6eeff1ZAQIC8vb1lZ2en1157Lc34pBUrVqhPnz5ydnaWu7u7unXrdtsaqlevrnr16sna2lotW7a09Az/9ttvSklJUbdu3WRra6vGjRurUqVKdzynCxcuqFChQndsd5Onp6eqVq0qGxsbFStWTM8995y2b98uSbKxsVFCQoKOHDkis9msMmXKyNXV1bLu0KFDio+Pl7OzsypWTP91+50sWLBAzz33nKpUqSJra2u1bt1atra22r17t6VN165dVbhwYeV6Ar42dC1UUNEx/341dybmrFwLFciw7Yrw9Rn24BNTdm0AACAASURBVPy8ZoMa1K0lWz64PHSuLnkUHXfR8jjm/CW55cuTYduft+1VU9/0vd+/bP9TgdXKy9bm8fua9VFmvnxeVnn+vdas8uaX+fL5DNtaV/h3iIp0oxc99cyJG0NczKm6vn+HTO4ls7tk3IL3zpzNNbe95eZLSToTf02FnOwzbLvywBk1KedueeyW215lC+ZWMWdH2ZhMCihdSH/FXMr2mh+2xzqMT5kyRZGRkdqzZ49Gjx6trl27KjY2Vr/99pu6du0qPz8/Va9eXfPnz9f58zfemGNiYuTu/u8vgoODg6W3+Ob6W28CvbVtRgoWLGj5OVeuXLp27ZpSUlIUExMjNze3NEH/bm4udXFxUWxs5mOt/uvo0aPq06ePateurWrVqmnSpEmWc61Zs6Y6d+6skJAQ1axZU6NGjVJ8fLwk6ZNPPtH69esVEBCgLl26aNeuXXd9zJuioqI0c+ZMywcib29vRUdHKyYmxtImu2+ofZR4li+rEyejdDIqWsnJyVqxer0C/NN/43Dk+N+6dDleVT2fSbduxap1GY6FRParWKqoTpyJ08nY80pOua6fI/aqXtWy6dodPX1WlxMSVaVMsXTrVkT8oSYZjCNH9kqNOiJTfjdZORe80dNdwU8pB9K/p1kVKCyrXI5KPXXo321PH5FyOUqONz54mUpWUOrZUw+tdvDemdNVdMurExeu6NTFRCVfT9XKg9GqXzp9p+LRuARdupasKu7Ot2zrrMtJKYq7cuMb9e0nz6t0/tzpts3pHuswfpO1tbUaN24sk8mkHTt26PXXX1eDBg20fv167dixQx07dpT5n68kXV1d0wxLuXr1qi5cuGB5XKhQIUVHR1se3/pzVhQqVEhnzpyxHFeSTp8+fcftatWqpY0bN+rKlSt3dZy3335bpUuX1sqVK7Vz504NGjQozTG7deumRYsW6aefftKxY8c0Y8YMSVLlypU1depUbd68WQ0bNtTAgQOzeIY3gnbfvn0VGRlp+ffbb7/p2WeftbR5HO+KzoyNjbVGDnpZfQa/peDnX1JQYB09XbqEPv3iG6295WakFeHr1bRhvXTPzanTZxQdc1beXnf+BgUPno21SSO6NNXLH81Rqzc/U+MaFfR0UVdNWbxW63b9eyPnzxF/KMinYvrX7+wFRcddkne5kg+5csicqqSV3yhXp6Fy6DtO1/dFyHz2lGzrtpG1h5elmU0FP6X8GfGfbc1KWj1PDs8Pk8OL70myUsqudQ+1/Ccd7505m43JpGH1y+mVsJ1q8+1mNfZwU5kCufXZ1kNad+TfzrmVB6IVVNY9zetnbbLSYP+y6rt4h9rP2SKzzGrjWdSI08hWT8T3NWazWatXr9alS5dUpkwZJSQkyNnZWfb29tqzZ4+WL1+u2rVrS5KCgoLUoUMH7dy5U56engoNDU0TXps2barp06erUqVKunr1qmbPnn1PNVWtWlXW1taaPXu2OnXqpHXr1un333+Xj4/Pbbdr2bKl5s+fr1dffVUjR45UqVKldPHiRS1YsEDPPPNMups4ExIS5OTkJCcnJx0+fFjz5s2zTH+4Z88emc1mVahQQQ4ODrKzs5PJZFJSUpJluE6ePHnk5OQkkynrn9vat2+v/v37q1atWqpcubISExO1bds2eXt7K3fux++T7d2oW8tHdWulfY37v5h2qFO/Xl0y3LZoYTetCbu33zc8GHUqe6hO5bSzB/VrHZDm8cut6me4bdGCLgr/aFB2lYY7uH54jxIPD02zLHnDorSPf12c4bapR/cqccZb2VYb7oz3zpytTslCqlMybW/4K35Pp3nc1y/je2n8niqg7zrXzLbaHgWPdc9437595eXlpWrVqmny5MkaN26cPDw89L///U+ffPKJvLy8NGXKFDVt2tSyjYeHh0aNGqXBgwerTp06cnR0VP78+WVnZyfpxjh0d3d3NWjQQD169FBQUJBlXVbY2dkpNDRUCxcuVI0aNbR06VLVr1//jvuys7PTrFmzVLp0afXs2VPVq1dX+/btdf78eVWuXDld+2HDhmn58uWqVq2aRo0apWbNmlnWJSQk6K233pKPj48CAgLk4uJiGVcfFhamwMBAVatWTfPnz9fEiROzfI6VKlXSO++8o5CQENWoUUONGzfWokWL7rwhAADAE8LKbOZ/L7idhIQE1ahRQytXrlTx4sXTrZ87d65++umne+4hv1X79u3VsWNHtW3b9r739bhLPnvE6BJwj67v33LnRnhkXV+38s6N8Eiy6/O20SXgPiQv+MjoEnAfHPt9mum6x7pn/F6tWbNGiYmJunLlisaPH6+yZcuqWLEbN2PFxMRox44dSk1N1ZEjRzRz5kw1bNjwno6zbds2xcbGKiUlRYsXL9b+/ftVpw5TZgEAADwpnogx41m1evVqDR06VGazWZ6envroo48sNxQkJyfrf//7n06ePKk8efKoefPmev755+/pOEePHtXAgQOVmJioYsWK6ZNPPpGrq6umTZum6dOnp2tfvXp1yw2WAAAAyPkYpoIciWEqORfDVHI2hqnkXAxTydkYppKzMUwFAAAAeAQRxgEAAACDEMYBAAAAgxDGAQAAAIMQxgEAAACDEMYBAAAAgxDGAQAAAIMQxgEAAACDEMYBAAAAgxDGAQAAAIMQxgEAAACDEMYBAAAAgxDGAQAAAIMQxgEAAACDEMYBAAAAgxDGAQAAAIMQxgEAAACDEMYBAAAAgxDGAQAAAIMQxgEAAACDEMYBAAAAgxDGAQAAAIMQxgEAAACDEMYBAAAAgxDGAQAAAIMQxgEAAACDEMYBAAAAgxDGAQAAAIMQxgEAAACDEMYBAAAAgxDGAQAAAIMQxgEAAACDEMYBAAAAgxDGAQAAAIMQxgEAAACDEMYBAAAAgxDGAQAAAIMQxgEAAACDEMYBAAAAgxDGAQAAAIMQxgEAAACDEMYBAAAAgxDGAQAAAIMQxgEAAACDEMYBAAAAgxDGAQAAAIMQxgEAAACDEMYBAAAAgxDGAQAAAIMQxgEAAACDEMYBAAAAgxDGAQAAAIMQxgEAAACDEMYBAAAAgxDGAQAAAIMQxgEAAACDEMYBAAAAgxDGAQAAAINYmc1ms9FFAFl1vn19o0vAPdq5wc3oEnAf9tvZGl0C7lEnv5NGl4D7MGdrMaNLwH3o//fsTNfRMw4AAAAYhDAOAAAAGIQwDgAAABiEMA4AAAAYhDAOAAAAGIQwDgAAABiEMA4AAAAYhDAOAAAAGIQwDgAAABiEMA4AAAAYhDAOAAAAGIQwDgAAABiEMA4AAAAYhDAOAAAAGIQwDgAAABiEMA4AAAAYhDAOAAAAGIQwDgAAABiEMA4AAAAYhDAOAAAAGIQwDgAAABiEMA4AAAAYhDAOAAAAGIQwDgAAABiEMA4AAAAYhDAOAAAAGIQwDgAAABiEMA4AAAAYhDAOAAAAGIQwDgAAABiEMA4AAAAYhDAOAAAAGIQwDgAAABiEMA4AAAAYhDAOAAAAGIQwDgAAABiEMA4AAAAYhDAOAAAAGIQwDgAAABiEMA4AAAAYhDAOAAAAGIQwDgAAABiEMA4AAAAYhDAOAAAAGIQwDgAAABiEMA4AAAAYhDAOAAAAGIQwDgAAABiEMA4AAAAYhDAOAAAAGIQwDgAAABiEMA4AAAAYhDAOAAAAGIQwDgAAABiEMA4AAAAYhDAOAAAAGIQwDgAAABiEMA4AAAAYhDAOAAAAGIQwDgAAABiEMA4AAAAYhDAOAAAAGIQwDgAAABjExugC7sXo0aPl5uamfv36ZWm7qKgoNW/eXJGRkbK2ts6m6h5d9/q84cGxqeojxxf6SyZrXVv9o64tmZtmvUP3frLx9JIkWdnZy8o5ny72ePbG44Kucur7hkwFXCWZFT92uFJjox/2KTzR8gdUUdl3e8jK2qSoOWt0PDQszXqPkG7KV7uiJMnawU62BZ21oWxPSVJg1DzF7zshSbp66qz2dJv4cIt/whWvX1n+b3eVydqkP+et067PlqVZX/t/nVW0ZgVJko2DnRwK5NWXnn0kSX2PfaO4v/6WJF2OOqcVPT96uMWD984c7qn6lVXn7a6y+uf62/mf68//luvP9p/r74t/rr9Xjn2jc/9cf/FR5/TjY3j9PZQwHhgYqHfffVe1atV6IPsLCQm5p+MWKVJEu3btuufjRkREqHv37nJwcJAkubq66qWXXlLbtm3veZ8P090+b/cjJiZGkydP1oYNG5SQkCA3Nzc1a9ZMvXv3Vps2bdS7d2+1a9cuzTZff/21wsLCtGjRomyvz1Amkxx7DVD8O0OUGherPO9PU3LkJqWePG5pkvj1FMvP9k1ay7qUh+WxU/+RurroW6Xs2SHlcpBSUx9q+U88k5XKjeupXR3e07Woc6qx8n2dXRmphAOnLE0Ojv7G8nOxXk2Up1JJy+PrV5O0rcGwh1kx/mFlslLdd7tr2fPjFH86Tu2Wh+jYqh06fzDK0mbTmDmWnyv1aKSCniUtj69fTdJ3Td58mCXjVrx35mhWJivVe7e7wv65/josD9HR/1x/G2+5/ir/5/pLuZqkBY/59ccwlSxydXXVrl27tHPnTo0cOVKjRo3SkSNHHvhxUlJSHvg+s9uFCxfUsWNHXbt2TfPnz9euXbs0c+ZMXbp0SSdOnFDr1q0VFhaWbruwsDC1bt3agIofLuunyys1+pRSY05LKSlK3rRGdt61M21v599ASZtWS5JMxUrIytr6xh8TSbqaKCVdexhl4x95qz2txKNndPV4jMzJ13VmyWYVbFIj0/ZurWvpzKJND7FCZMa1ahldPHZGl07EKjX5ug4t3apSjatn2t6jZU0dDNvyECvE7fDembO5/ef6O7h0q0pz/aVhWBhPSkrSe++9J39/f/n7++u9995TUlKSZf0XX3xhWff999+rXLlyOn78xqfg4cOHa9KkSZKkuLg49enTR97e3vLx8dHzzz+v1NRUvfHGG4qKilLfvn3l5eWlL774QidPnlS5cuUsQffChQsaMWKE/P39VaNGDb3yyit3Xb+VlZXq1asnZ2dn7d+/X5KUmpqqzz//XA0bNpSvr68GDBigCxcuWLZZsmSJAgIC5OvrqylTpigwMFCbN2+WJIWGhuq1117TkCFDVK1aNS1evFiXL1/WyJEj5e/vrzp16mjSpEm6fv26JOn48ePq0qWLqlevLl9fXw0cOFCSZDabNXbsWNWsWVPVqlVTcHCwDhw4kO55k6TvvvtOjRo1ko+Pj/r27aszZ85Y1pUrV07z5s1T48aN5e3trTFjxshsNt/2OZk5c6acnJw0ceJEFStWTJJUuHBhvfXWWypfvrxatmypHTt26NSpf3sSDx06pAMHDqh58+Z3/dznVKb8hZR6LtbyODUuVlYFCmXctqCbTK6FlfLHjW9yrAsXV2pCvJyGhCjPhC/k0LWvZOKz9MOUyz2/rkadszy+FnVO9u75Mm5brKAcnnJV3MY/LMtM9raqsXKsvH96VwWbemd7vfiXk3s+xUfFWR7Hn46TUyavXe6iBZSnuKtObdprWWZtb6t2P4aoTdjbKhWUeYhA9uC9M2dzcs+ny3d5/eUpWkB5i7vq5C3Xn429rTr8GKJ2j/H1Z9hv5NSpU/Xbb78pLCxMS5cu1e+//67PPvtMkrRhwwbNmjVLM2fO1KpVqxQREZHpfmbOnCk3Nzdt2bJFmzZt0uDBg2VlZaWJEyeqSJEimjZtmnbt2qUXX3wx3bZDhw5VYmKifvzxR23evFk9evS46/pTU1O1evVqnT9/XiVKlJAkffvttwoPD9fs2bP166+/ytnZ2TI05NChQxozZowmTpyoX3/9VfHx8WnCryStXr1aTZo0UWRkpIKDgzV8+HDZ2Njol19+0ZIlS7Rp0yZ9//33kqSPP/5YtWvX1vbt27VhwwZ16dJFkrRx40ZFRkZq5cqV2rFjhyZPniwXF5d09W/ZskUffvihJk+erI0bN6po0aIaPHhwmjbr1q3TwoULtXTpUq1YsUK//vrrbZ+TLVu2qFGjRjJl8kbn7u4uX1/fNL3jYWFhqlu3rvLnz3/bfT9pbGsHKmnr+n+/TrW2lu0zlZT4zVRdHt5XJtfCsqvfxNgikSm3VrUUszxCSv33A+zm6v20PWik/nj5E5UN6S6HEm4GVojMeLSoqcM/bZP5ltfu25oDtbD5aIW/OkW1/9dFeUu4Glghbof3zpwto+vv65oD9V3z0frl1Smq85hef4aF8WXLlqlfv34qUKCA8ufPr379+mnp0qWSpBUrVqhNmzby8PCQg4ODXn311Uz3Y2Njo9jYWEVFRcnW1lbe3t6ysrK64/FjYmK0YcMGjRkzRs7OzrK1tZWPj89dbeft7a3KlSurf//+Gj58uCpUuHHTwfz58zVo0CC5u7vLzs5O/fv318qVK5WSkqKff/5ZAQEB8vb2lp2dnV577bV0dVatWlUNGzaUyWRSfHy81q9fr5EjR8rR0VEFChRQjx499OOPP1rOOyoqSjExMbK3t5e3t7dleUJCgo4cOSKz2awyZcrI1TX9L+6yZcvUtm1bVaxYUXZ2dho8eLB2796tkydPWtq8+OKLyps3r4oUKSJfX1/99ddft31uLly4oEKFMu6tuKlVq1aWMJ6amqply5Y9EUNUpBu9OaZbenNM+QvJfEtvz63sagcqaePqf7c9F6uUY4dufE2bel3J2zemGROJ7Hc1Ok65ihSwPLYvUkDXos9n2NatVfohKjfbXj0eo/Ob/0wznhzZKyH6vHIX+fcDf+7C+ZWQyWv3dAs/HfrPV+Q32146EauorftUsGKJ7CsW6fDembMlRJ9Xnru8/jxa+OnAba6/U1v3qdBjeP0ZFsZjYmJUpEgRy+MiRYooJibGss7d3d2yrnDhwpnup1evXipRooR69uypBg0a6PPPP7+r40dHR8vZ2VnOzs5ZqtvV1VWRkZHauXOnunbtqq1bt1rWRUVFqV+/fvL29pa3t7eaNWsmk8mkc+fOpTsnBweHdD3Wt66PiopSSkqK/P39LfsbPXq04uJufNXzxhtvyGw2q127dmrevLkWLlwoSapZs6Y6d+6skJAQ1axZU6NGjVJ8fHy684iJiVHRokUtj52cnOTi4pKmt/7WYO3g4KCEhITbPjcuLi6Kjc34DfKmxo0bKzY2Vrt371ZERIQSExNVr169227zuLh+aL9MhYvJ5Oou2djc6MGJ3JyunanIU7JyyqPrB/79mu764b9k5ZhbVnlv/L7aeFZLc/MSst/lXYflWNpduZ4qJCtba7m1qqWzKyPTtXN8uohsnJ10MfKAZZmNs5Os7G7cL2+bP49cfMop4cDJdNsie8T8dkTOJd2Vp3ghmWyt9XQLPx1dtTNdO5cyhWXv7KToHQcty+ydHWX657XLlS+33L3L6vzBU+m2RfbhvTNnO/Of68/jPq6/wt5lFfcYXn+GTW3o6uqqqKgoeXjc+IR6+vRpSw+uq6trmlB4+vTpTPeTO3duDR8+XMOHD9eBAwfUvXt3VapUSTVr1rzt8d3d3XXx4kVdunRJefPmzXL9dnZ2GjJkiJo0aaLw8HA1bNhQ7u7uGjt2rKpXTz+mydXVVUePHrU8vnr1aprx5JLS9JTf7F3funWrbGzSv0yFChXSu+++K0mKjIzUCy+8oBo1aqhEiRLq1q2bunXrpnPnzmngwIGaMWOGZUz5rfXcOnb7ypUrunDhgtzc7v2r85o1a2rVqlXq379/pkNVHBwcFBQUpCVLlujatWtq3ry57Ozs7vmYOUrqdV358mPlfnOiZDIpae0KpZ48plzPvaDrh/cr+Z8/Lna1A5W8ec1/tk1V4rdTlXv0R7KyslLKkQO6tnq5ASfx5DJfT9X+EV/Ja/5Iydqk0/PWKWH/SZUe2l6Xfjuisytv3CDm1qqWzoSlDQpOHkVV/oMXZU41y8pkpWOhYWlmYUH2Ml9P1a+jvlbw7KGysjbprwXrdf7AKdV4va1i9xzVsX+CgUeLmjq0dGuabfM9XVT1xvWUOTVVViaTdk1ZlmYWCDwEvHfmaObrqdow6mu1/Of6+3PBesUdOCWf19sq5pbrr2yLmjqYwfUXcMv1t+Mxvf4eWhhPTk7WtWv/3sHcvHlzTZ06VZUqVZIkTZkyRcHBwZKkJk2a6M0331TLli1VpEgRy1jyjKxdu1alS5fWU089pTx58sja2toSagsWLKi///47w+1cXV1Vt25djRkzRqNHj5ajo6N2796tGjUynx3hv+zs7NSzZ09NmTJFDRs2VKdOnTR58mSNGzdORYsWVVxcnHbu3KmGDRsqKChIHTp00M6dO+Xp6anQ0NDb3hDp6uqq2rVra9y4cRo4cKAcHR118uRJRUdHy8fHRytWrJCXl5fc3d3l7OwsKysrmUwm7dmzR2azWRUqVJCDg4Ps7OwyDMbPPvusBg8erODgYJUpU0YfffSRKleubLnx8l688MILWrZsmYYNG6aBAweqaNGiOnPmjL766iu1bt1a5cuXlyS1bt1ar776qpKTkzVr1qx7Pl5OlLIrQpd2pb0H4uqCmWkffz8r42337NDlIb2yqzTchXOrd2vL6t1plh2Z8H2ax0c/WJhuu4uRBxRR/41srQ23d2Ltb5q79rc0y7Z/+EPax5PST68aveOgFjQaka214c5478zZjq/9Tcf/c/1t+8/1ty2T62/eE3D9PbRhKi+99JIqV65s+ZeUlCRPT0+1aNFCLVq0UMWKFS2zmdSrV09du3ZVt27d1KhRI1WpUkWSMuxBPX78uF544QV5eXnpueeeU6dOneTn52c55tSpU+Xt7a0vv/wy3bYTJkyQjY2NmjZtqlq1aunrr7/O8nm1bdtWUVFRWrNmjbp166bAwED17NlTXl5e6tChg/bs2SNJ8vDw0KhRozR48GDVqVNHjo6Oyp8//217hSdMmKDk5GQ1a9ZMNWrU0GuvvWYZBvL777+rffv28vLy0ssvv6w333xTxYsXV0JCgt566y35+PgoICBALi4u6tUr/ZtQrVq1NGDAAL366qvy9/fX33//nWamlXvh4uKiefPmycbGRh06dJCXl5e6d++uPHnyWG5ylaQaNWood+7ccnd3V+XKle/rmAAAADmZlflO89U9Ag4fPqxnn31Wv//+e4ZDNnKihIQE1ahRQytXrlTx4sWNLifHOd++vtEl4B7t3MAsIjnZfjtbo0vAPerkx30KOdmcrff+zTWM1//v2Zmue2Qn21y1apWSkpJ08eJFTZw4UQEBATk+iK9Zs0aJiYm6cuWKxo8fr7Jly97XsBAAAADkbI9sup0/f76GDx8ua2tr1ahRQ//73/8eynGnTZum6dOnp1tevXp1zZgx4772vXr1ag0dOlRms1menp766KOP7moaxkfJ6NGjtWzZsnTLg4ODLXOqAwAA4O7kiGEqwH8xTCXnYphKzsYwlZyLYSo5G8NUcrYcOUwFAAAAeNwRxgEAAACDEMYBAAAAgxDGAQAAAIMQxgEAAACDEMYBAAAAgxDGAQAAAIMQxgEAAACDEMYBAAAAgxDGAQAAAIMQxgEAAACDEMYBAAAAgxDGAQAAAIMQxgEAAACDEMYBAAAAgxDGAQAAAIMQxgEAAACDEMYBAAAAgxDGAQAAAIMQxgEAAACDEMYBAAAAgxDGAQAAAIMQxgEAAACDEMYBAAAAg2QpjCclJWnSpElq0KCBqlevLknauHGjZs+enS3FAQAAAI+zLIXxsWPH6sCBA/rggw9kZWUlSfLw8NC8efOypTgAAADgcWaTlcbh4eH65Zdf5OjoKJPpRo53c3PTmTNnsqU4AAAA4HGWpZ5xW1tbXb9+Pc2yuLg4ubi4PNCiAAAAgCdBlsJ4kyZNNGzYMP3999+SpJiYGIWEhKh58+bZUhwAAADwOMtSGB80aJCKFSumFi1a6NKlSwoKCpKrq6v69euXXfUBAAAAj60sjRm3s7PTyJEjNXLkSMXFxSlfvnyWGzkBAAAAZE2WwrgkJSYm6vjx47py5YqOHTtmWV6tWrUHWRcAAADw2MtSGF+yZIlCQkJka2urXLlyWZZbWVlp3bp1D7o2AAAA4LGWpTA+ceJEhYaGqnbt2tlVDwAAAPDEyPLUhj4+PtlVCwAAAPBEyVIYHzBggMaNG6e4uLjsqgcAAAB4YmRpmErJkiX1ySefaO7cuZZlZrNZVlZW2rdv3wMvDgAAAHicZSmMDx06VC1btlSzZs3S3MAJAAAAIOuyFMYvXLigAQMGMLc4AAAA8ABkacx4mzZtFBYWll21AAAAAE+ULPWM79mzR3PmzNHUqVNVsGDBNOvmzJnzQAsDAAAAHndZCuMdOnRQhw4dsqsWAAAA4ImSpTDeunXr7KoDAAAAeOJkKYxL0tmzZ7Vnzx6dP39eZrPZsrxdu3YPtDAAAADgcZelMB4eHq433nhDJUqU0KFDh/T000/r4MGDqlatGmEcAAAAyKIshfHJkydr7Nixatq0qWrUqKElS5bohx9+0KFDh7KrPgAAAOCxlaWpDaOiotS0adM0y1q3bq0lS5Y80KIAAACAJ0GWwniBAgV09uxZSVLRokW1a9cunThxQqmpqdlSHAAAAPA4y1IYb9++vXbs2CFJ6tGjh7p166aWLVuqU6dO2VIcAAAA8DjL0pjxl156yfJzq1at5OPjo8TERJUpU+aBFwYAAAA87rI8teFNqampcnd3t/xsMmWpkx0AAAB44mUpjO/du1chISHav3+/rl27Jkkym82ysrLSvn37sqVAAAAA4HGVpTA+fPhwBQQEaOzYscqVK1d21YT/s3ffAVXV/x/HX/cycy9AwZkDVyqK+lW0FDVzb1NzhFppOVrukXu0jMwyc+RK03KlaG7NUUoOcqGQirIEVyIq6/7+4MdNQjNKOALPx1+ccS/vy4dz7ut+7ud8DgAAAHKEdIXx0NBQvfXWWzKZTBlViQJYdwAAIABJREFUDwAAAJBjpGugd7NmzbRv376MqgUAAADIUdLVM37v3j0NGjRItWrVUpEiRVJte//99x9rYQAAAEB2l64wXq5cOZUrVy6jagEAAABylHSF8UGDBj1yn3nz5qWajxwAAADAg5ksFovlcT5hzZo1deTIkcf5lEAaY0r3MLoE/Eu/JF41ugT8ByduhRhdAv6lfgU8jC4B/8HXN48bXQL+g9DrJx+67bHfqecxZ3sAAAAg23rsYZxpDwEAAIB/hnvYAwAAAAZhmAoAAABgkMcexj09PR/3UwIAAADZ0iOnNjx48OA/eqJ69epJkr766qv/VhEAAACQQzwyjI8ZM+aRT2IymbRjx47HUhAAAACQUzwyjO/cuTMz6gAAAABynHSPGY+Pj5e/v7/8/PwkSbGxsYqNjX3shQEAAADZ3SN7xu8XGBiogQMHyt7eXpGRkWrZsqUOHz6stWvX6pNPPsmoGgEAAIBsKV094xMmTNCQIUO0ZcsW2dom5/jatWvr119/zZDiAAAAgOwsXWE8KChI7dq1k/TnnTZz5cqle/fuPf7KAAAAgGwuXWHczc1NJ06cSLUuICBAJUuWfKxFAQAAADlBusaMDx06VK+99pq6deum+Ph4ffnll1q5cqUmT56cUfUBAAAA2Va6esYbN26s+fPn69q1a6pdu7ZCQ0M1e/ZsNWjQIKPqAwAAALKtdPWMS1LlypU1YcKEDCgFAAAAyFkeGcZ9fX3/0RMNHTr0PxcDAAAA5CSPDOMRERHWn+/du6etW7eqatWqcnNzU1hYmH777Tc9//zzGVokAAAAkB09MoxPnz7d+vNbb72ljz76SM2bN7eu27p1q7Zs2ZIx1QEAAADZWLou4Ny7d6+aNm2aap23t7f27NnzWIsCAAAAcoJ0hfFSpUpp+fLlqdatWLGCecYBAACAfyFds6lMmTJFgwYN0vz58+Xi4qKIiAjZ2dlp9uzZGVUfAAAAkG2lK4xXrlxZP/74o44fP64rV67IyclJNWrUkJ2dXUbVBwAAAGRb6RqmIkkmk0kWi0VJSUnWZQAAAADpl66e8eDgYA0cOFD37t1T0aJFFR4eLgcHB82dO1dly5bNqBoBAACAbCldYXzixInq2rWr+vXrZ+0RX7BggSZMmKClS5dmSIEAAABAdpWuYSpnzpyRj49PqqEpffr00ZkzZx57YQAAAEB2l64w7uzsrEOHDqVa5+/vL2dn58daFAAAAJATpGuYyltvvaXXX39djRo1kqurq0JDQ7Vnzx598MEHGVUfAAAAkG2lq2e8SZMmWrt2rcqXL6/Y2Fi5u7tr7dq1ae7KCQAAAODR0tUzfuvWLW3atEmnTp1SbGysLl68qMOHD0uSFi5cmCEFAgAAANlVusL40KFDlZiYqGbNmsnBwSGjagIAAAByhHSF8WPHjunnn3+Wvb19RtUDAAAA5BjpGjNeq1Yt/f777xlVCwAAAJCjpKtnfMaMGXrllVdUvXp1FS5cONW2QYMGPdbCAAAAgOwuXWF81qxZioiIUPHixRUTE2Ndf/9NgAAAAAD8M+kK45s2bdKPP/7ITX4AAACAxyBdY8ZLlCghW9t05XcAAAAAD5GuZN2uXTu9/vrr6tmzZ5ox4/Xq1XushQEAAADZXbrC+PLlyyVJH3/8car1JpNJO3bseHxVAQAAADlAusL4zp07M6oOAAAAIMdJ15hxAAAAAI8PYRwAAAAwCGEcAAAAMAhhHAAAADAIYRwAAAAwCGEcAAAAMAhhHAAAADAIYRwAAAAwCGEcAAAAMAhhHAAAADAIYRwAAAAwCGEcAAAAMAhhHAAAADAIYRwAAAAwCGEcAAAAMAhhHAAAADAIYRwAAAAwCGEcAAAAMAhhHAAAADAIYRwAAAAwCGEcAAAAMAhhHAAAADCIrdEF4O/NnTtXly5d0tSpU40uBY9B+eeqqdX43jLbmOX/7S7t/eKHVNtbjuupp+tVliTZOTood5F8mlLtFUnS5OBligwMkSTdCL2qZa98lLnFQ7Ubeer1CQNktrHR5hWbtfLzVam2D3zvNVWvV12S5PiUgwoULqD2VTtZt+fKk0sLds7T/h8P6rNxczK19pyucZMGmjxjtGxszFq+5Dt99sn8VNsnThspr4Z1JElPPfWUijgVknuputbtefLm1t6fN2rLph0aPXxKptYOzp1ZXaMmDTRp+kiZbWy0Yun3mvOX42/C1BGqbz3+HFXYqZAql65n3Z4nb27tPrhBW/x2auzw7JeHCOOZwNvbW3fu3NGOHTuUK1cuSdLq1au1YcMGLV269G8fO2DAgAytbeTIkdq4caPs7OwkSWXKlNHIkSNVp06dDP29OZHJbFKbST5a1HO6/oi4qoEbpuj0tiOKCgq17uM3eZn15//1eV6uVUpbl+PvxumzlqMzs2Tcx2w2a/CUNzSixyhFhUdrzsbZOrDtZ4WcC7Hu88XEL60/t3+5rcpVLZfqOV5+t7d+++VEptWMZGazWdM/HKeu7fspPCxSW3at0tbNu3Q2MNi6z3ujZ1h/7vfqS6parVKq5xgxZoh+PuCfaTXjT5w7szaz2aypH4xR9w6vKDwsUn47v9XWzbt07r7jb8KYmdaffV7pkeb4GzZ6sH4++Gum1ZzZGKaSSZKSkrRkyRKjy3igfv366ejRozpy5Ii6d++uwYMHKzEx0eiysp3iNcrp2sVIXb90RYnxiQr44aAqPV/roftXa1tfxzccyMQK8Xfca7gr7EKYwkMilBCfoN0bdsvr+XoP3b9xu8bauX63dbn8M+VU0Kmg/Pdm3zeUJ5VHrWo6/3uIQi5eVnx8vNZ976fmLb0fun/7Tq209js/63K16pXl5FREe3btz4xy8RecO7M2j1rP6MLvl6zH3/o1fmresvFD92/fuaXWff/n8fdM9cpyci6svTuzb5sSxjNJv379tHDhQv3xxx9ptk2ZMkXPPfecatasqY4dO8rf/8/el9mzZ+vdd9+VJPXv31/Lli1L9di2bdtq69atkqTg4GD5+PioTp06at68ufz8/JQeJpNJrVu31o0bNxQdHS1JCgkJUe/evVW3bl3VrVtX77zzjvU1zJ8/X4MHD07zWqZMSf4K99atWxo9erQaNGighg0batasWdaQf/HiRfXs2VO1atVS3bp19eabb6ar1qwon0tB3Qy7al3+I/ya8rsUeuC+BdyKqFAJJ/1+4KR1na2DnV7fMEWvrZ2oSs97Zni9SK1I0cK6EhZlXY4Kj1bhokUeuK+zm7OKlnDRsf3HJCUfWwPGvaovJ3+VKbUitWLFnBUWGmFdDg+LVLFiLg/ct3gJV5UsVVz79v4sKbntJkwdoYnj3s+UWpEW586srWgxF4WFhluXw8MiVfQhx59biWIqUbK49u/9RVLy8Td+yjBNHvdhptRqFMJ4Jqlatarq1KmjBQsWpNn2zDPPaN26dTp06JBat26toUOH6t69e2n2a926tTZu3GhdDgoKUlhYmBo1aqTY2Fj17dtXrVu31oEDBzRr1ixNnDhRQUFB/7jGxMRErVu3TsWLF1eRIskhw2Kx6LXXXtNPP/2kzZs3KyIiQrNnz5aU/EHgp59+sobzhIQEbdq0Se3bt5eUPATG1tZWW7du1bp167R//36tXr1akuTr6ysvLy8dPnxYe/fuVc+ePf9xnTnBM23q6YTfIVmSLNZ1H3oN0edtx2rVkDlqNb6XCpV0NrBC/J3GbRvpJ799SkpKkiS17d1Gv+w8rOiIaIMrw6O079hSGzf8aG07n/7dtWPrXoWHRRpcGf4Jzp1ZW7uOLbVpw1br8denf3ft3PZTtj/+COOZaMiQIVq2bJmuXbuWan27du1UsGBB2draqm/fvoqLi9P58+fTPL5p06Y6c+aMQkOTx8n98MMPatasmezt7bV79265ubmpU6dOsrW1VeXKldW8eXNt2bLlkXUtXLhQnp6e8vDw0LRp0zR06FDZ2NhIkkqVKiUvLy/Z29urUKFC8vHx0eHDhyVJzs7O8vT0tP6On376SQULFlTVqlUVHR2tPXv2aPTo0cqVK5cKFy6sl19+WZs2bZIk2draKiwsTFeuXJGDg4M8PbN/b8UfkdeV37WwdTlfsUK6GXntgftWa1NPAX/5mvWPyOuSpOuXruj8z6dU7L4xkch40RFX5ezqZF12KlZEVx8Srhu3fS7VEJXKtSqp/ctttezAYr029hU169RE/Uf2zeiS8f/Cw6/I1a2odbmYq4vCwx/85t6uUwut/W6TdblW7RryeaWHDgds1/jJw9WlWzuNee/tDK8Zf+LcmbVFhEfK1a2YdbmYq4siHnb8dWyh9fcNUalVu7p8Xumhn49v1bjJ76rzi2016r23MrzmzMYFnJmoQoUKatSokebNm6eyZcta1y9YsEDfffedrly5IpPJpJiYGF2/fj3N4/PkyaPnnntOmzZt0quvvqqNGzdah4SEhoYqICAgVahNTExU27ZtH1lX37599dZbb8lisejcuXPq27ev8ufPr+eee07R0dGaOnWq/P39dfv2bVksFuXLl8/62A4dOmjFihXq2rWrNmzYoHbt2kmSwsLClJCQoAYNGlj3TUpKUrFiyQfksGHD5Ovrq86dOyt//vzy8fFR586d0/kXzVpCjwercOmiKljcSX9EXlO1NvW0ashnafYrUtZVT+XPrZAj56zrHPPlVvzde0qMS1CugnlVspa79s7dmOaxyDiBxwPlVtpNRUu4KDriqhq1baRpg2ek2a9E2RLKkz+PTv16yrpu+pA/L056vkszVahWQfNnLMyUuiEdO/Kbni5bSiVLuSk87Irad2qp1/sPS7NfufJlVKBAfvkfOmZd98arw60/v9ijvarXqKqpEz/OlLqRjHNn1nbsyAmVKVtSJUq6KSL8itp1bKk3Xkl7/JUtX0b5C+RLdfwNfnWE9eeu3durmkcVTZ84K1PqzkyE8Uw2ZMgQdejQQX37JveK+fv7a/78+fr6669Vvnx5mc1m1a5dWxaL5YGPb926tT777DPVrl1b9+7dU926yVNvFStWTLVr19aiRYv+dW0mk0kVKlRQzZo1tWfPHj333HP6+OOPZTKZ9MMPP6hAgQLavn27Jk2aZH1M06ZNNWHCBJ09e1a7d+/WsGHJB1jRokVlb2+vn3/+Wba2af/NnJycrB8k/P395ePjo9q1a6tUqVL/uv4nXVJikn4Y/7VeXjJSJhuzjqzarSvnQtXkrc4K/e13ndl+RNL/9+z8cDDVY53LuardtH6yWCwymUza+8WGVDMJIOMlJSZp9rg5mrFsmsw2Zm35dqsunr2oPu/01tmAszq4LXmMceO2z2n3hj0GV4v7JSYmavSwKVrx/XzZ2Ji1YtkaBZ4J0vDRg3Xs6Alt3bxLktS+U+oLx/Bk4NyZtSUmJmrs8Kn65vt5MtuY9e3ytTp7Jljvjhqk48dOatv/H3/tOrbQ+jWbDa7WGCbLw1IfHhtvb29NmTJF9evXlySNHTtW27ZtU4UKFdS/f3+NGTNGa9euVf78+TVv3jzNmTNHCxYsUP369TV79mxdvHhRH36YfPFCXFycvLy8VLVqVZUvX16jRydP1xQTE6M2bdpo6NChatWqlSTp9OnTyp07d6pe+L8aOXKkXFxc9NZbyV/7BAcHq0+fPnr99dfVo0cPDR06VHnz5tXEiRMVHR2toUOHKiwsTHv37rU+x9ixY3X8+HEVLFgw1YwxAwcOlJubm958803lypVLly9fVkREhOrUqaPNmzfLw8NDRYsW1blz59SpUydt2rRJJUqU+Ed/0zGle6SjBfAk+SXx6qN3whPrxK2QR++EJ1K/Ah5Gl4D/4Oubx40uAf9B6PWTD93GmHEDvPHGG4qNjZUk60wjzZs3l7e3txwcHKxDOR7E3t5ezZo104EDB9S6dWvr+jx58mjBggXy8/NTw4YN1aBBA3344YeKi4t7ZD0LFiyQh4eHatSooX79+qljx47q1q2bJGnQoEE6deqUPD099eqrr+r5559P8/j27dvr7Nmz1iEqKd5//33Fx8erZcuWql27toYMGaKoqOTZKH777Td16dJFHh4eGjhwoMaMGfOPgzgAAEB2Qc84/rOwsDC1aNFC+/fvV548eTLld9IznnXRM5610TOeddEznrXRM5610TOODJOUlKRFixapZcuWmRbEAQAAsgsu4MwBWrVqpbCwsDTrJ06c+I9mW3mY2NhYeXl5ydXVVfPnz/8vJQIAAORIhPEcIGVu78ctV65cOnr0aIY8NwAAQE7AMBUAAADAIIRxAAAAwCCEcQAAAMAghHEAAADAIIRxAAAAwCCEcQAAAMAghHEAAADAIIRxAAAAwCCEcQAAAMAghHEAAADAIIRxAAAAwCCEcQAAAMAghHEAAADAIIRxAAAAwCCEcQAAAMAghHEAAADAIIRxAAAAwCCEcQAAAMAghHEAAADAIIRxAAAAwCCEcQAAAMAghHEAAADAIIRxAAAAwCCEcQAAAMAghHEAAADAIIRxAAAAwCCEcQAAAMAghHEAAADAIIRxAAAAwCCEcQAAAMAghHEAAADAIIRxAAAAwCCEcQAAAMAghHEAAADAIIRxAAAAwCCEcQAAAMAghHEAAADAIIRxAAAAwCCEcQAAAMAghHEAAADAIIRxAAAAwCCEcQAAAMAghHEAAADAIIRxAAAAwCCEcQAAAMAghHEAAADAIIRxAAAAwCCEcQAAAMAghHEAAADAIIRxAAAAwCCEcQAAAMAghHEAAADAIIRxAAAAwCCEcQAAAMAghHEAAADAIIRxAAAAwCCEcQAAAMAghHEAAADAILZGFwD8G+GKM7oE/EuX710zugT8B9GxfxhdAv6l8AKcN7OyyNs3jC4BGYSecQAAAMAghHEAAADAIIRxAAAAwCCEcQAAAMAghHEAAADAIIRxAAAAwCCEcQAAAMAghHEAAADAIIRxAAAAwCCEcQAAAMAghHEAAADAIIRxAAAAwCCEcQAAAMAghHEAAADAIIRxAAAAwCCEcQAAAMAghHEAAADAIIRxAAAAwCCEcQAAAMAghHEAAADAIIRxAAAAwCCEcQAAAMAghHEAAADAIIRxAAAAwCCEcQAAAMAghHEAAADAIIRxAAAAwCCEcQAAAMAghHEAAADAIIRxAAAAwCCEcQAAAMAghHEAAADAIIRxAAAAwCCEcQAAAMAghHEAAADAIIRxAAAAwCCEcQAAAMAghHEAAADAIIRxAAAAwCCEcQAAAMAghHEAAADAIIRxAAAAwCCEcQAAAMAghHEAAADAIIRxAAAAwCCEcQAAAMAghHEAAADAIIRxAAAAwCCEcQAAAMAghHEAAADAIIRxAAAAwCCEcQAAAMAghHEAAADAIIRxAAAAwCCEcQAAAMAghHEAAADAIIRxAAAAwCCEcQAAAMAghHEAAADAIIRxAAAAwCCEcQAAAMAghHEAAADAIIRxAAAAwCC2RhcA5CRVn6uhHuN9ZLIx66dvd8jvi3Wptncb97Iq1qsiSbJ3dFC+Ivk1qFof63bHPE9pyrZPdHTrIS1/b0Gm1g6pQeN6GjP1HZltzPpu2Xp9NXtxqu0jJ72lug08JUlPPeWgQkUKqU55b+v23Hlya9O+b7Vj8x5NHvVBptae0zV/vpE+/niSbMxmLVy0Qu9/MCfV9o8+mKDnGtWXJOXK9ZScnQqriHNllSzppu9WL5DZbJadna3mzFmkeV8tNeIl5GicO7M2jr+/RxjPAvr3769WrVqpQ4cOf7ufh4eHNmzYoBIlSmRSZUgPk9msnpP666Oek3Qt4prGb5ihY9v8FRZ02brPyslfW39u0qeFSlYpk+o5OrzTTWcPncqsknEfs9ms8TOHq2+XQYoMi9TqrYu188e9Cj573rrPjPGzrD/37NdVlZ5xT/UcQ0cOkP/Bo5lWM5KZzWZ96jtVL7TsrsuXw/XzQT/9sHGrTp8+Z93nnWETrD+/8bqPatSoKkkKD7+iBg3bKi4uTrlz59Lxozv1w8atCg+PzOyXkWNx7szaOP4ejWEqj5G3t7eqVasmDw8PeXp6qlu3blqxYoWSkpL+0/POnz//kUFcko4ePZphQbxVq1by8PCQh4eHKlWqpGeeeca6PHfu3Az5ndnN0zXK6crFCEVduqLE+AT98sN+1Xi+9kP3r9u2gX7ZsM+6XKrq08pXpIBO/nQ8M8rFX1SrWUUh5y/p8sVQxccnyG/tNjV54bmH7t+qY3NtWvujdblKtYoq7FRI+3f/khnl4j51ansoOPiCzp8PUXx8vFatWq+2bZo/dP9uL7bXt98m97zGx8crLi5OkuTg4CCzmbfNzMa5M2vj+Hu07PmqDDR37lwdPXpUu3bt0iuvvKKvvvpKY8aMMbqs/2zTpk06evSojh49Kk9PT40fP966PGDAAOt+CQkJBlb5ZCvgUkjXwqKty9fDr6qgS6EH7lvYrYiKlHDW6QMnJEkmk0kvju2jVVMXP3B/ZDyXok4KD/2zNyYiPFIuxZweuK9r8aJyK+mqn3/yl5TcfiMmvqn3J/hmSq1IzdWtqC5dDrMuXw4Nl6tr0QfuW7Kkm0qXLqGdu/Zb1xUv7qojv27Thd8P64MP52S7XrknHefOrI3j79EI4xkkb968atKkiT755BOtXbtWZ8+eVVxcnGbOnKlGjRqpfv36Gj9+vO7evWt9zPbt29WuXTvVrFlTTZs21d69eyVJvXr10urVqyVJFy9eVM+ePVWrVi3VrVtXb775pvXx7u7uunjxoiTp1q1bGj58uP73v/+pcePG+vzzz6099GvWrFH37t01c+ZM1a5dW97e3tqzZ8+/ep2XL1+Wu7u7Vq9erUaNGqlPn+Qxet99951atGih2rVrq1+/fgoNDbU+Jjg4WD4+PqpTp46aN28uPz+/f/W7s7M6bRrI3++gLP/fZo17NVfAriO6HnHN4MrwT7Ts8Ly2/rDDesz18OmsPTv2KzL8isGV4VFe7NpO36/ZlOobzcuXw1SzVjO5V/JS715d5OxcxMAK8Xc4d2ZtOfX4Y8x4BqtWrZqKFi0qf39/fffddwoJCdG6detka2urd999V3PmzNE777yjgIAAjRgxQp9++qnq1aunqKgoxcTEpHk+X19feXl5acmSJYqPj9dvv/32wN87efJk3bp1S9u3b9eNGzfUr18/OTk5qUuXLpKkgIAAdejQQT///LO+/fZbjRkzRj/99JNMJtO/ep2HDx+Wn5+fzGaztm/fri+//FJz585VqVKlNG/ePL3zzjtauXKlYmNj1bdvXw0ZMkRfffWVzp49Kx8fH1WoUEHlypX7V787q7gReU2FXP88iRQsVljXIx/8BlGnjZeWjZtvXS5b010ValeUd6/mcsjlKFs7W92LvavvZi7P8LqRLDIiSsXcXKzLRYu5KDI86oH7tmz/vCaPfN+6XKN2NdWqW0M9Xu6sXLlzyc7eVrdv39HHUz7L8LohhYVGqERxV+tycbdiCguLeOC+Xbu205AhD/42Mzw8UidOBqpBg7pas2ZThtSKtDh3Zm0cf49Gz3gmcHZ21s2bN7Vq1SqNHj1aBQoUUJ48efTaa69p06bkf6jvvvtOnTp1kpeXl8xms1xcXFS2bNk0z2Vra6uwsDBduXJFDg4O8vT0TLNPYmKi/Pz89M477yhPnjwqXry4fHx8tGHDBus+rq6u6tq1q2xsbNShQwdFRUUpOjo6zXP9U4MHD1auXLnk6OiolStX6tVXX1XZsmVla2urAQMG6PTp0woNDdXu3bvl5uamTp06ydbWVpUrV1bz5s21ZcuWf/27s4rzx4PkUrqYihR3lo2dreq28dKxbYfT7Fe0rKty58+t4COB1nVfvemrYV4DNbzB61o1bYkOrNnDm0km++3oKZV6uqTcSrrKzs5WLTs0084f96bZr0y5UsqfP6+OHg6wrhs2cJy8a7ZRE892en+Cr9av8iOIZ6LD/sdUrlwZlS5dQnZ2duratZ1+2Lg1zX7u7mVVsEB+HfzZ37rOza2YHB0dJUkFCuSXl1cdnT0bnGm1g3NnVsfx92j0jGeCyMhIJSYm6s6dO+rYsaN1vcVisX4VEx4erueee/jFYCmGDRsmX19fde7cWfnz55ePj486d+6cap/r168rPj5erq5/fhJ1dXVVZOSf46yKFPmzl+Gpp56SJMXGxv67FyipaNE/x3+FhYVp2rRpmjlzpnWdxWJRZGSkQkNDFRAQkOpDRGJiotq2bfuvf3dWkZSYpGXj5+vtJWNltjFr36qdCjt3We3felEXfgvWse3JJ6C6bRro0A/7H/FsyGyJiYmaPPJ9Lfj2U5ltbPT9NxsUFPi7Bo94TSeOndau/w/mrTo8r03rthlcLe6XmJiooW+Old+mb2RjNuvrxd/q1KmzmvDeu/L/9bg2bkxurxe7ttOq1etTPbZSxXJ6//3xslgkk0n6+OO5OnHijBEvI8fi3Jm1cfw9mslisViMLiK78Pb21pQpU1S/fn3ruoCAAHXt2lXr1q3Tiy++qK1bt8rFxSXNY8ePHy9HR0eNHj06zbZevXqpbdu21iEmKfz9/eXj46ONGzeqVKlScnd319atW1W8eHFVr15d69atsw79+Pbbb7Vx40YtXbpUa9as0erVq7VixQrrc6U8tlSpUo98nffXc/nyZTVp0kQnT56UrW3yZ7t+/fqpXbt2DwzYGzdu1Pfff69FixY98vf8nb6lOz96JzyRDsReNLoE/AdBN8IevROeSL1d6xldAv6DJWEHjS4B/0FCXOhDtzFMJYPExMRo165devvtt9W2bVtVrFhRXbp00bRp03T16lVJyT3mP/30kySpc+fOWrNmjQ4ePKikpCRFRkYqODjtVzGbN29WRETyWKv8+fPLZDKlmerHxsZGL7zwgmbNmqWYmBiFhoZq0aJFmdb73K1bN82bN0/nziXPIXrr1i1t3rxZktSoUSMX66+mAAAgAElEQVRduHBB69atU3x8vOLj4xUQEPDA1woAAJDdMUzlMRswYIBsbGxkNptVrlw5+fj4qFu3bpKSh5jMmTNHXbt21fXr1+Xi4qLu3burYcOGqlatmqZPn65p06bp8uXLKlKkiMaPH59m3Phvv/2madOmKSYmRoULF9aYMWMeOLf4uHHjNHnyZDVt2lQODg7q0qWLOnXqlCl/g2bNmun27dt6++23FRoaqrx586p+/fpq0aKF8uTJowULFmjGjBmaMWOGLBaL3N3dNWrUqEypDQAA4EnCMBVkSQxTyboYppK1MUwl62KYStbGMJWsjWEqAAAAwBOIYSpIxcPD44Hrv/rqqwdOowgAAIB/jzCOVI4ePWp0CQAAADkGw1QAAAAAgxDGAQAAAIMQxgEAAACDEMYBAAAAgxDGAQAAAIMQxgEAAACDEMYBAAAAgxDGAQAAAIMQxgEAAACDEMYBAAAAgxDGAQAAAIMQxgEAAACDEMYBAAAAgxDGAQAAAIMQxgEAAACDEMYBAAAAgxDGAQAAAIMQxgEAAACDEMYBAAAAgxDGAQAAAIMQxgEAAACDEMYBAAAAgxDGAQAAAIMQxgEAAACDEMYBAAAAgxDGAQAAAIMQxgEAAACDEMYBAAAAgxDGAQAAAIMQxgEAAACDEMYBAAAAgxDGAQAAAIMQxgEAAACDEMYBAAAAgxDGAQAAAIMQxgEAAACDEMYBAAAAgxDGAQAAAIMQxgEAAACDEMYBAAAAgxDGAQAAAIMQxgEAAACDEMYBAAAAgxDGAQAAAIMQxgEAAACDEMYBAAAAgxDGAQAAAIMQxgEAAACDEMYBAAAAgxDGAQAAAIMQxgEAAACDEMYBAAAAgxDGAQAAAIMQxgEAAACDEMYBAAAAgxDGAQAAAIMQxgEAAACDmCwWi8XoIgAAAICciJ5xAAAAwCCEcQAAAMAghHEAAADAIIRxAAAAwCCEcQAAAMAghHEAAADAIIRxAAAAwCCEcQAAAMAghHEAAADAIIRxAAAAwCCEcQAAAMAghHEAAADAIIRxAMgGLBaL0SUgA9G+QPZFGAfwUElJSWnWEQqeLHFxcZL+bCvaJ3t50DH4d+uR+f56zHEMPlmyQvuYLE9iVQAMl5iYKBsbG4WEhOj06dOKjo5Whw4d5OjoKLOZz/FPgrNnz2ru3LlKSEjQ008/rSZNmuiZZ54xuiw8JklJSTKbzfr999+1fv162dvbq2TJkmrTpo2k5FBhMpkMrjJnSzlP4sl0f/vExcUpMTFRTz31lMFVpUUYB/BQgYGB6tu3rxo2bKiQkBCZTCb16tVLTZo0kZ2dndHl5WghISHq0aOHXn75ZRUtWlSnTp3Shg0b9P3338vFxcXo8vCYBAcHq0ePHmrfvr2SkpJ06dIl2dvb69NPPzW6tBwv5cOSJE2ZMkWxsbG6du2a+vfvL3d3d+XNm9fgCnO2+z+sjhgxQjExMQoMDNSgQYNUvXp1lSlTxuAK/0T3FoA0LBaL4uLiNGvWLPXv318zZszQN998o19//VWRkZEE8SfAvn375OXlpf79+6t169bat2+fvL295eLiotu3bxtdHh6TpUuXqkuXLho1apTefvttXbhwgQ9bT4iUIP7222/r+PHj6tKli0qXLq1JkyZpx44dBleHlCA+atQoXbhwQePHj9fbb7+t2bNna8uWLU/UUC/COACrxMRESdKdO3dkb2+va9eu6dlnn5Ukde7cWS1atFCfPn0UFBSk2NhYI0vNsS5duqTg4GDdu3fPGsrat2+vcuXKadKkSYqOjta2bdt0584dgyvFv5ESEKKiohQbGytbW1vVqFFDktS9e3dVqVJFY8aM0YULF3T48GEjS82xUs6TkhQZGambN29q+fLl8vDwkLOzs6Kjo/Xss88qKSnpiRyfnN3d3z7x8fG6efOmPv/8c7m4uOjatWu6ffu2OnXqZO10ehIQxgFISu4Nt7Gx0enTp9W+fXtduXJF5cuX165du6xhb9asWZIkX19fbd++3eCKc57Lly+rS5cuioqKUokSJbRkyRI1a9ZMnp6e+vjjjyVJ06ZN04kTJ57IcZH4e/ePEe/evbvOnz+vuLg4LVy4UN26dVPVqlX10UcfSZLmzJmjEydOGFxxzpOUlGQdg3z69GnZ2Njo5MmTslgsmj9/vhYuXKjly5erUKFC8vPz0/Xr1w2uOGdJeR+TpPXr18tsNisgIEAhISH6+uuvNXfuXC1fvlzOzs5atWqVzp8/b3DFyQjjAKxj62JiYrRhwwZ1795dzs7OqlixolavXq28efNq+vTpkqThw4crMjJSLVu2NLjqnOXq1atasmSJ+vbtq//9739q2rSp+vfvr0uXLqlly5Y6d+6cRo4cqaCgII0cOdLocvEvmM1mXb16Vd988421F3zEiBEymUwKCgrSlClTFBcXp1GjRikoKEi9evUyuuQcJTEx0To0ZcCAARozZoyKFCkib29vvfrqq1q0aJHWr1+vMmXKaPfu3fryyy9148YNg6vOORITE1MNTfn0009lY2Ojbt26aebMmfriiy/0ww8/qGzZstq3b5/mz5+v+Ph4g6tOZmt0AQCeDNevX1ebNm1UvHhxjR8/XpLUq1cv3bx5UyEhIWrTpo2efvppRUREaPny5bK1tWUmgUwSFxenFStWaN26dXrhhRes6/v37688efJoxowZKlGihGxsbPT999/TNllUTEyM5s6dq40bN2rChAmSJAcHB02fPl2jR4/Wiy++qHz58kmSVq1aRTtnspS/88GDB1WkSBF9/vnnkiQPDw8FBgbqhRde0L179/Tjjz9q3LhxmjRpkp5++mkjS85RUtpn8+bNcnR01Lp16yRJNWvW1IkTJ9SgQQP98ssvunv3rqZPn6733ntPVatWNbJkK2ZTAWD1ySefaO7cuZoxY4bat29vXR8ZGang4GAVKFBA7u7usrGxUUJCgmxt+TyfWYKCgrRy5UodPXpUb7zxhry9va3b/vjjD+XLl8/6DQcBLev46/SEfn5+WrJkiQoUKKCpU6eqcOHC1m3BwcHKkyePnJycZDabOQYNsGfPHg0cOFDFihXTggULVLp0aUnJQyKOHz+uI0eOyM3NTe3bt1ezZs2YfjKTXbx40freNWfOHNWvX1+S5O/vr8DAQG3atEmVK1eWl5eXGjdu/MS0D2EcyKFSAlt8fHyq2VFmz56tr776Sl9++aXq1av3wMfeP6UXMs61a9cUFhamAgUKqGjRorp586a+/PJLhYaGqnPnzmrcuLEkpQplT8qbCx4t5Ri8ffu2YmNj5eTkJEnav3+/VqxYIScnJ73++uvW9ffjGMwcD/o7L1++XF988YXeffddPf/888qVK5d12+3bt2VnZyd7e3vrxZscjxnnQR0PAQEBGjZsmLy9vdWvXz8VKVLEuu2v50rpyWgfmwkp34UByDFSLkI6d+6cJk6cqO3bt2vDhg2qW7euGjdurPj4eE2ZMkXVq1dXiRIl0jz+STh5ZXdnzpxR3759deTIEX377bc6efKkqlSpogYNGigoKEiHDh2S2WxWuXLlUoUF2iZrSDkGz5w5o3fffVdr167V7t27FRUVpbZt2ypv3rz69ddfdfLkSVWsWFF58uRJ9XjaOePdH/SCg4N1+fJlubi4qFq1ajKbzZo/f76KFi2qkiVLWgOevb299TEmk4l2ykD3t8+BAwd07tw5mc1mubu7q3r16po7d65u376tihUrWi9o/+u58klpH8I4kAOZTCZdunRJ3bt3l7e3t2rVqqWAgAAtX75ctWvXVsuWLXXnzh2NGzdOjRo1krOzs9El5yg3btzQG2+8oZdeekkTJ05UqVKlFB4ero0bN8rb21sVK1bUyZMndevWLevXsMhaTCaTIiMj1bdvX3Xv3l29e/dWUlKSDh48qMDAQHXv3l12dnbat2+f7ty5o5o1axpdco6TEtzefPNNrVu3Tj/88IPWrl2r4sWLq23btkpMTNTChQtVoEABlSlThiFDmSylfQYPHqwff/xRJ0+e1Lx582Rra6vmzZurevXq+vLLLxUdHa0qVao80TNMEcaBHCZlGMPOnTtlb2+v0aNHq3z58mrXrp0OHz6sFStWqEePHqpXr55cXFzUsGFDvg7PZHfu3NHu3bvVv39/5c2bV6VKlZKTk5OOHj2qfPnyqVatWipXrpxeeOGFJ6ZnB+l37tw5BQYGauzYsSpcuLCqVKkiR0dH7d+/X9WrV7fOW926dWva2SCfffaZTp06pQULFqhbt276/ffftWXLFj399NNq0aKFrl+/rsWLF6tNmzbKnTu30eXmOKtXr9bhw4e1evVqdezYUUWKFNGqVatksVjUtGlTlS1bVvPmzVPz5s1VsGBBo8t9KN5hgRwi5WYiKW/q9+7d0/Hjx3Xt2jXrPtOnT1dCQoL8/f0lSV26dJGtra0SEhIyv+AcKCYmRjExMYqPj1dwcLDOnTsnKfnr2EqVKsnR0VFHjhyRJJUsWVImk+mJuosc/l7KzUji4uJksVh09+5dXbx40Xp82dvbq379+rpw4YKOHj0qSapXr57MZjPtnEnuv4zOYrHo6tWratu2rfLkyaPcuXPrvffeU4kSJfT+++9LSu6VXbx4Md8eZpK/HgdRUVEqW7as9VzYvn17devWTb6+voqOjla9evW0fv36J35WG8I4kAOkzI8bHByszz//XElJSfLw8FDp0qW1d+9e6+3T7e3tVbBgwTQ9PHz9mvHOnDmjwYMHKyAgQM7OzurTp49GjhypY8eOWcdFWiyWVBcjSeJbiywiZXzr2bNn9cEHH+j8+fOqV6+enJycNHDgQOt++fLl09NPP53qokCJds4M989THR8fL5PJpFy5cmnbtm26deuWdb9XXnlFFovFekOf4sWLG1JvTnP/PO8RERGSpAoVKujMmTM6c+aMzGazLBaL2rRpo3LlylnvQpw3b17Dav6nOLqBbC7ljmRnz57VSy+9pLt37+ratWtyd3dXhQoVtHnzZvn6+mrPnj0aO3asEhMTVaFCBaPLzlHOnz+vvn37qn79+qpTp44kqU+fPurWrZt69uypoUOH6uWXX1ZQUJB8fHwMrhb/RsoF07169VKRIkWsUxbOnDlTN27cUNu2bTVv3jwNHjxYly9fTjV1JTLe/XfWfPfdd+Xr66u4uDg1btxYefPm1bJly6zhOzg4ONUsKwwhynj3t8/QoUM1bdo03bhxQxUqVFD58uW1atUqHTt2TCaTSYcPH1ZkZGSWah+mNgRygJiYGA0aNEhNmjRRr169Ur2RrFmzRocPH9aVK1dUsGBBTZ8+XXZ2dsxVnYkWLlyoq1evatiwYWmmUjt48KAuXrwoSercuTM3esmiEhMTNW7cOJUsWVIDBgxINQXl3bt35evrK4vFIovFomHDhtHOBnnnnXcUHh6uqVOnqkyZMpKSpzI8dOiQTp48qcaNG2vdunWaPHlyqhtwIXOMHj1aISEh8vX1VcGCBWU2m7Vv3z79+OOP2rVrl6pWraqAgACNHTs2S90lmu+egRzAzs5OJpNJ1atXt77hp4S+jh07qmPHjrp9+7Z1eAo3E8lcFy5c0NWrVyUlD0eIi4uTvb29Ll26JE9Pz1TzvRPQsqbExERdunRJVapUkaRUx+Ddu3c1YsSINPvTzpnrt99+U2hoqJYtW5bqw9BLL72khg0bau/evXJwcNAXX3whT09P5vTPZGFhYbp06ZI++OADFS5cWAkJCTKbzWrQoIGeeeYZdejQQTExMXrzzTdVsWLFLNU+DFMBsqGUC8Xi4+MlJfeMh4aG6tKlS9YLXcxms6KiorRo0SIlJSVZg7jFYiGIZ4Lo6GidP39ekvTss8/Kzs5OAQEBkpLH7kvS119/rV9//TXV4whoWUPKMXjr1i3duXNH9vb2qlGjhvWDl9lslo2NjUwmk1asWGH99iMF7Zzx/noxoJ2dneLi4nTt2jVr0EtKStLt27fl4uKinj17qkuXLvL09DSo4pwl5RhKYWtrq9jYWOtwoZS7DcfExCg2NlY1a9bUs88+q4oVK1q3ZxWEcSCbuf9CsZEjRyo0NFSFCxfWyy+/rEmTJungwYPWO25+/PHH2rdvX6rHZ6UTWFZ15swZ+fj4yN/fX/fu3ZO7u7vu3bun1atXa9WqVYqIiNDw4cN16NAh1a5d2+hykU4px+CZM2f09ttv68CBA5KkqlWrKigoSBs2bNCpU6ckScOHD9fOnTsfeHMtZJz7LwYMCQmRxWKRg4ODrly5ooCAAGuHhNlslp+fnz766KM04Z1zZca5/5shf39/3b17V/ny5VNSUpJWr14tKfkDq42NjXbv3q1x48YpJiZGWXXkNfOMA9mM2WxWYGCg+vTpo//973/y8vKSvb29nnnmGVksFo0ZM0b+/v5as2aNwsPDtXjxYtna2mapr/Syst9//109e/bUSy+9pJ49e8rGxkb58+eXh4eHgoKCrGP44+LitGTJEuvX5cymkXWYzWadPXtWvXv3VuvWrdWlSxdJst4t9cCBA5o9e7YOHTqkqKgoLV++XLa2tkpKSuIYzAT3Xww4ZMgQ/frrr6pevbqKFy8uBwcHjRo1Sm5ubrp7967OnTun8ePHq0+fPipbtqzBlecM97fPoEGDtG/fPtWpU0eFCxdW7dq1NWXKFJ0/f163b9/W2bNnNXHiRA0ePFiVK1fOsscPF3AC2czt27c1YMAANWvWTL1795Yk7du3T3ny5JG7u7suXLigixcvysbGRt7e3rKxsWGMeCb65ptvFBUVpaFDhyohIUGzZs1SbGysKleurC5duujOnTuKi4tTvnz5ZDKZaJssKCkpSZMmTZKbm5teeeUVJSUl6fvvv9fdu3fVokULFSlSRCdPnpSjo6PKlCkjs9lMOxtgyJAhunLlir744otUN4TZvHmz5s6dq1y5cslkMql379564YUX6LDIZMOHD9fly5e1cOFCOTo66u7du3J0dFRkZKSmTJmi+Ph42djYqEOHDmratGmWbh+OfCCbyZ07t0qWLKnY2FgFBwdr2LBheuqppxQZGan27dtr0KBBqlSpknX/xMREQkAmiouL09q1a+Xq6qqlS5fK1dVVlSpV0rRp0+Tu7q5q1apZb9vM+P2s686dO7pw4YJ++eUXTZkyRUWLFtWdO3e0aNEirV+/3nohp5Qc3mnnzHXmzBlFR0dr5cqVkpJnlTp37pxy586tHj16qEGDBjKZTLp3754KFy6cZYc/ZFVRUVGKjo7WzJkz5ejoqCVLlujo0aNydHRUx44d9emnn8pkMik2Nla5cuXK8u3DMBUgG7FYLEpISNCVK1e0f/9+7d+/X5UqVZKvr69sbGx08uTJNNNxMfwhc6VcxJeYmKgaNWpo7Nixqlu3ro4fP64GDRqkuqlPVu3lyelMJpNKlSqlZcuW6datW6pYsaKmT5+uWrVqKSgoSI0aNZKjo2Oq/ZG57O3ttWXLFu3fv19HjhzRkiVLVLVqVe3atUtms1l16tSRvb299eZLJpOJdspEuXLl0qFDh7RkyRKdOnVK69evV6dOnXT8+HHdvHlTDRs2lJR8UWdK22Tl9uGjOJCNmEwm2dnZqUOHDmrRooXu3bunYsWKSUqetosZGoyVclHSX/tARo4cqejoaJUrV86YwvBYpdw4a+XKlanupDl79mz98ccfyp8/v4HVQZIcHR3Vv39/63SFK1eulJubm+Li4qzTjMI4JpNJffv21YEDB2Q2mzVw4EAVK1ZMsbGxOnbsmOLj461T9mYHjBkHsrH4+HiFhIToo48+UkhIiNauXSs7O7ssPbYuq/m7v3VAQIBmzZqlmzdv6ttvv5WdnV2am/4ga7NYLDp48KAWLFigK1euaM2aNbRzJkrPfO179+7VO++8o08++UReXl4ZXBnSa9u2bRoxYoQ+/PDDbHeHWs4EQBb01/lXHyY+Pl6BgYFydXW1BvGEhASCeCaIiYmR9PdDEKpVq6bOnTtr1apV1rYhoGUvKUNWqlatmuoYpJ0zTlxcnN5//3398ccfsrGxeej5MqUvMjIyUitXrtTw4cM1YcIEeXl5ZfkxyNnJ1atXtW7dOk2fPl1TpkyRt7d3tmsfesaBLCokJEQXLlzQs88++7e9PylXoEuy3tkRGeuPP/7Q6NGj1b9/f9WoUeOB+/DtRNaVcrz9kzb86z60e8Y7deqUpk+fLicnJ02aNEl58uT523NkbGysTpw4IQcHB+tdiiXG8j9uD/rf/6ffEAUHB8tisahcuXLZsn34aA5kUTt37tSoUaN0+/bth77JJCUlWYN4QkICQTyTpNwRLjIyUlLaO/391ZkzZ3Tt2rXMKA3/UcocyMHBwfriiy/02Wef6e7du//osREREY/8X8C/FxcXp4sXL6py5coaOnSo4uPjNWbMGMXExPxtD3muXLlUs2ZNVa9eXZL49jCDpPxNFyxYYL1xj9ls/ke93KVKlbJeU5OYmJjt2ocwDmRRL774ourUqaPt27dLShv4LBaLtcdh6dKlGjVqVKbXmFO5urqqUaNGmjZtmqKiotL0/NzfQ/T1119r6NChRpSJdErpxQsLC1OHDh106dIlrV27Vq+88opOnz6dJlT8tZ0nTZr0j4M70sdisWjq1Kn6+OOPdfbsWXl6eqpPnz5KSkrSmDFjdOvWrYcG8pQ53mNiYpSQkGC9QzEev3nz5umTTz6Rr6+vvvnmG0nJIf3vAnlK+9y4cUOBgYHZchpQwjiQhdy5c8f6Zu7g4KCiRYtq8+bNklJPUXh/CFi+fLk+++wz9enTJ/MLzkHCw8O1bds263Lv3r1Vq1Yt663QUz4s/bVt5s6dqw8++ECFChXK/KKRLmazWZcvX9aOHTs0YsQITZ8+XTt27FC+fPk0bdq0VIH8/rtpphyDAwcOVO7cuY18CdmWyWRS9+7ddfv2bS1dulSBgYGpAvnYsWOtgfz+jov7g56Xl5dOnjxp4KvInu7evauAgACFhoYqMDBQY8eO1bBhwzR//nwtX75cUnL7Pehbo/vbp1mzZoqOjs7s8jMF84wDT7iU8Hbz5k31799fFy5c0N27d1W2bFl5eHho8eLFMpvNqlq1aqr9peQQ4Ovrq0WLFlm34/G7d++eNm3apIkTJyo8PFw3b95UxYoVFRQUpH379qlNmzbWN5uUD03Lly/Xp59+qoULF+qZZ54x+BXgn0hISNDnn3+uzz77TJ6enqpVq5YkqVWrVtq+fbt2796tsmXLysnJKVU7+/r6avHixRyDGSgpKUlOTk6qXLmy/Pz8dOHCBbm5ualatWpydnbWsWPHtGvXLjVs2FAODg5KTEy03mzp+vXr6tKli95//33VqVPH6JeSrdy+fVtt2rTR008/LQ8PDzk7O8vT01NVqlRR/vz59eWXX8rW1lbVqlVLc21Fyg3prl+/rs6dO2vGjBlq0KCBga8m4xDGgSdYYmKizGazIiMjlT9/fhUqVEjx8fGaOXOmrly5ori4OLm5uSk6Olr16tVLFfa+/vprffbZZ/r6669T3e0Pj9fvv/+un376SU2aNNFLL72kc+fOad++fdqwYYMaNWqkxYsXq1ixYnJ3d7e+2XzzzTf65JNPtGjRItrmCXf/h1uz2ayyZcsqKipKW7duVbt27ax3S23VqpVWrVqlq1evqlGjRpKSh4fNnj2bds5gKUPykpKSVLhw4YcG8oCAAPn5+VlvumQ2m3X9+nV17dpV48eP17PPPmv0S8l2tmzZotOnT2vSpEmytbVVwYIFlS9fPtnY2Kh06dIqWLBgqkB+5MgRXb9+Xc7Oztb2efHFFzV+/HjrjX6yI8I48IRKuVDszJkz6tu3rySpY8eOqlmzpho1aqQLFy7I399fS5cu1blz51SnTh3rDX5CQ0O1ePFiTZs2jd64DBQYGKju3burUqVKcnd3l4uLi2rXrq3WrVvr7NmzOnnypM6dOycbGxs1b97cGuyOHTum119/nYD2hEv5MHzt2jVFREQoJiZGxYsXl5eXl06cOKElS5bI29vbOvSkU6dOatiwoUwmk+Li4rRgwQJNnjyZYzADpbSRJOvf3dnZWe7u7tqyZUuqQF6oUCEFBASoYsWKcnJyUkxMjJo2baqpU6cSxDNIVFSULly4IBcXF40ePVp2dnaqUKGC9QZ1pUuXVv78+bV48WKdPHlSM2fOVNOmTVWyZEnduXNHLVq00IQJE/Tcc88Z/VIyFFMbAk+w8PBw9erVS/3791e3bt0k/XkRWVxcnEwmkxYtWvR/7d15XJVl2sDx3zmHwyKbbAKyyiKIjgsjapY6iZNLZo4mKahouZRrTrkrkfapAZdQU3NJyZHSF4xm0pyaNx3aMFxxCwVEBVEUEGURzgHO+wdznoGy9+2dSY7I9f2r4Hn43Mfnc5/neq7nuq+br776Cg8PD15//XXatGmDXq+npqYGGxsbE3+CR9e9e/eYPn06Tz75JJMnT77vMaWlpWRkZLBkyRI2bNggG4m0IMYHp6ysLBYsWICFhQVqtRoPDw/Wrl1LRUUFy5cv58qVK2zatAk3NzflXGOdq2zs82A1ble4evVqbt++TV1dHdOmTcPPz4+srCzi4+Px8vJi3LhxBAcHc+fOHWUH1JycHKqqqujataspP8YjLyYmhq+//hqNRtOk4UDjufHGG2/w0Ucf8c477zB06FCgob94WVkZ/v7+Jhl3c5JvCSEeYufPn8fPz4+xY8cqC8OMraDMzc3RarVMmzaN6dOnU1BQQFVVFQBarVYC8QespqaGqqoqpYaxtrYWaAjijG0K27Zty5AhQ5g8eTLp6ekmG6v4ZYzzBxqyrKWlpcyfP58JEyawZcsWlixZwuXLl5k8eTI2NjYsX74cJycnEhISmvwdY7eHR6392sPGGIjPnDmTI0eO4O3tjYWFBePHjyc3N5fg4GAWLlzIhQsX2LVrFyfSF5wAACAASURBVJWVlUogDhAQECCB+ANk7Fyj0WioqamhU6dOnDhxgpqaGqWsCCAjI4OPPvqIdevWMXToUOVe5+Tk1CoCcZBgXIiHWkVFBXfv3gX+9QoWGjI6JSUlynH9+vXj7t27nDp1yiTjbI1sbGywsbHhyJEjGAwGJQDT6XR89dVX3L17V8n8FBYWcv369Udu17hHyd///nfi4uKUOQZw+/ZtLCwsCA8Px9HRkW7durF582YqKyv55JNPcHR05O233+att96679+UYPzBS0pKoqioiJSUFKZPn463tzelpaU899xzXLx4kaCgIGJjYxk7dqx0smlmxoel559/nsOHD2Nra8v777/P0aNHlfKiuro6ZWG0sZSvNZJgXIiHxP3aOoWGhpKVlcWOHTsAlE17Nm3apLSEAsjMzOTWrVt07NixeQbbSlVXVyvb3JuZmdGrVy+OHj3K4cOHlZaTS5Ys4cCBA9ja2mIwGKiuriYvL48XXnhBgrOHWNeuXXn++ecxNzdHr9cDDfNNrVZz9epVoOGth5OTE25ubpSVlQHg7OzcJMsnHqwf9wnv2bMnL7/8MgCbN29m586dfPHFFwQFBTFq1CiysrIIDg6WDLgJBQYGYmFhweLFi2nTpg179uzhyJEjSpnRY4891mSL+9b4PSkLOIV4CBi/lK5cucK+ffs4ffo0KpWKjh074uvrS1xcHNeuXePmzZskJiaSnZ3NmjVrlMyrXq8nKioKDw8PE3+SR5exP+4HH3xAfn4+Li4uhIeHc/z4cb777jvee+890tPTyc/PJzExUelnbG5uzrPPPtukplg8XOrq6rC1tcXFxYWrV6+yfv16AgMD8fT0JD09nQMHDtC/f38sLS0xMzNj//79eHh40L17d+VvtMYAork1rhFPTEzE2dkZPz8/vLy8uHTpEhs3bmTr1q34+vqSk5ODvb09/v7++Pj4mHjkrZvxYdXS0pInnniC9PR0vvvuOxwdHfHx8VHmjkqlarXzSBZwCmFixoViFy5cYPLkyXTu3BkrKyu+//57Nm7cSM+ePcnMzGTTpk24uLig1WqVVenGhWLiwcrJySEqKoqJEyfSpUsXUlJSqK2t5eWXX6ZLly4UFBRw/PhxnJ2d6du3LxqNpsm1adweTzx8Gi8mu3HjBpGRkYSFhbFkyRLs7e2ZNGkS1dXVuLi4UF9fz9WrV0lNTZW5ZyIzZsygpKSEBQsWKL3ec3NziYmJYeHChej1epYsWUJiYiLu7u4y/x4SxnlWWVlJTEwML7zwgnSU+icJxoV4CJSUlDBhwgSef/55oqOj0el0PPHEE1RUVPDuu+8ycOBAdDqdUqYCTbNE4sGpqakhPj4eBwcHZs2aBcDQoUOpq6sjODiYiRMn0rNnzybnyLVpOYzXKj8/n8rKSoKDgykqKuKll17Cz8+P2NhYbG1tSU1N5fbt20DD7qpmZmZynU1gx44dfPnll03K9ADy8vJYtWoVZWVl5ObmsnTpUkaMGGGiUbYu95sHP9dJyHisdBpqSoJxIUzE+KV0+/ZtNBoNH374IVOnTkWlUjFy5Ei6deuGi4sL27dvZ+3atQwaNMjUQ251rly5glarJTs7m6CgIFxcXBgzZgxdunRh6tSpjBkzhuDgYGbNmvWTgFw83AwGg7JZTFZWFlOnTmXUqFFMmzYNa2trrl+/zssvv4y/vz+LFy/G2dm5yfkSiJtGQkICBoOBefPmUVNTg4WFhfK769evc+vWLczMzAgJCZGMeDNoPA9Onz5NdXU1PXr0QKvV/uw5er3+f/19aySPJUKYgHFDn+zsbCZNmkRhYSGRkZFoNBqWLVtGYGAgK1eupFOnTnh7eysLOEXzKSgoYNiwYZw9e5YBAwbg5ubGwYMHcXd3Z8WKFXh5edGrVy/69u1LaGioqYcrfqHCwkKgoT5VrVaTn5/PlClTmDFjBvPmzcPa2prq6mrc3d3ZunUrV65cYenSpdy8ebPJ35FA/MFrvCjW+PBUWlpKdnY2ABYWFkr3m5SUFOrq6ujatSshISEmGW9rZJwHc+bMYcWKFcTFxTFhwgQOHjzIvXv3fnJ8bW0tWq2W0tJSli5dqiyWbu0kGBfCBNRqNcXFxRw+fJhRo0YRHByMnZ0d0FAW0atXLwAOHTrE1KlTf/JKVjxYxcXFfP7558yYMYOnnnpKCQpu3bpFQUEB5eXlvPrqq1hYWDBlyhTpptFCHDt2jE2bNimdUKChl39YWBjjxo2jtraWxYsXM2/ePFavXk27du1Yv349jo6OP8mMiwer8c6apaWlFBYWolKpmDp1KqdOnWLdunVAQ8eb48ePs27dOqW/v5FkxZvHtm3buHnzJnv37mXfvn1UVVWRkpLyk39/4zqasrIyIiIiGDhwoGTI/0nKVIQwgcrKSiZPnkxJSQlr166lW7du6HQ6DAYDq1at4uzZsxgMBu7evctf//pXtFqt1Ng1A+PX4euvv87f/vY3evfuzdq1a5UbRkVFBcOGDcPd3Z2amhqSk5Pl2rQghYWF6PV6fHx8qKiowMbGhhMnTrBhwwbc3NzIycnB29ubvn378qc//Ym1a9fSr18/5Xy5zs2jcXnJK6+8wt27dzl37hxjx45lwIABVFdXM3fuXAIDA2nXrh3Hjx9n0aJFPP300yYeeevw4xKtt99+G39/fyIiIti6dSu7du1i7969ODs7o9PpsLW1VUpTysrKGD16NLGxsU3mVmsnrQ2FaCb19fWoVCrq6uqwsLBAq9Vy6NAhrK2teeyxx9BoNJiZmeHu7o63tzft2rUjLi4OrVYr9anNpLKyEgsLC8LCwrh16xbXr1+nXbt2eHh4oFarMTc3JzIykl69ejF9+nTMzMyora2Va9MCGAwG7OzsaNu2LTdv3mTNmjXU19cr3W8cHR0JDQ1l7ty5hISEcOLECQIDA/H391eCQ8m0Ng/jv/PKlSu5dOkS27dvZ8iQIaxevRq9Xs/48eMZOXIkGo2GTp06MW7cOPr16yc14s3E+EBaXFxMmzZtOH78ODdv3uTkyZPs3buXxMREfHx82L17NydOnCA0NBQzMzNKS0uJiIggJiaG/v37m/hTPFykL5MQzUStVlNUVMQnn3zCiBEjGDlyJGq1mvj4eGxsbJg6dSrQsEFCYGCgcp4E4s2joqKC3//+98yaNYuoqCjmz59PTEwMycnJqNVq5YHJwsICPz8/oOEBS9rbtQzGdRrGRZv37t3j4MGDWFpa8swzzyjHAMybN49r164RHh4OSLlDc/nxd92dO3dYuXIllpaWfP3111RVVTFt2jR0Oh3t2rUjKiqqyflynZpPcnIyCQkJHD58mF69erF+/Xry8vLYuXMn/v7+fPvtt2zbto24uDhl3sXExLBo0SIJxO9DMuNCNKO0tDQ+++wz7ty5g6+vL7/97W9xdXVl69at1NbW3nchoLwWbx7m5uZYW1vz9ttv4+LiQvfu3Xn88cf55ptvOH36NJaWlvj6+ja54cvNv2UwBnm5ubmkpqbSvXt3wsLCyMzM5PTp06jVavz9/cnKyiIhIYErV66wd+9epX2hzMEHz/iQBLBu3Tp69erFli1bcHZ25ujRo2zfvp1du3bh5eXF7t27uXv3rmzmY0I+Pj6cOXMGa2trBgwYQFlZGSqVioyMDM6ePcvGjRtZunQp4eHhyhuLgQMHyi7RP0OCcSEeoB/fyDt27Ihareb777/nxo0bdOjQgR49euDi4sKbb76Jl5cXQUFBJhxx61NRUaH0b//Nb36Dm5sby5cvx9XVlR49etC3b1/2799PXV0dffv2NfFoxb9DrVZz8eJFIiMj6dmzJx06dMDNzY3AwEDOnDlDVlYWlpaW9OzZEwcHB2bOnKmUIMmbjwevcS3+qlWr2L9/P5MmTUKj0bBv3z4OHjzI/v378fT05LvvvmPVqlUMHz6c9u3bm3jkrYOxxPLHTpw4wblz5xg8eDChoaF4eXlhb2+Pt7c3zz//vFI6BA2JC5lLP0+CcSEeILVazaVLl/jmm2+UIDs4OBidTkd6ejpFRUV07NiRrl270r17d5588knJwjWj27dvs2LFCkpKSvjNb34DQKdOnfD09GTZsmX4+fnRuXNnfv/73/PEE09IJryFqqmpYfHixYwcOZIpU6ZgY2MDgJ2dHR07duTs2bOkpaXh6+tLjx49lO44Uh7WPIzzKikpievXrxMbG0vbtm2xsLCgoKAAFxcXCgoKyM3N5c0332Tp0qUMGDDAxKNuPYzXJz4+XvmZo6MjQUFBJCQkYG9vT0hICK6uroSEhBAQEICbm5tyrnxv/t8kGBfiAWicSUhPT+e1117Dw8ODTp06AQ0BX0FBAR988AEVFRV07tyZoKAg1Gq1vBZvJnq9HoPBwPnz5zl37hz37t1T+hO3b9+eL7/8kj179tClSxcCAwNRqVQ/myESDzedTse+ffuYOHEizs7O1NbWKoup7ezs6NSpE/fu3WPIkCHK9ZXr3LxqamqIiYnhq6++IiQkhKCgIJycnPD396d9+/ZkZGRgb29PVFRUk9IH8eD8+F70t7/9jaNHj/Lxxx8rGytZWlpSVFREaGio0rtf/P9JMC7Er6zx9tqXLl2if//+eHt7s2jRItzd3ZsEfEePHqVz585Nsq7yZfbgZWdnk5CQQGhoKKGhody4cYMjR46g1+vp1KkTFhYW3Lx5kzFjxjBo0CDlmsjNv2X4caCm1WrZt28fOp2O3r17o1arlYerPXv20KNHD3r37i0PXCZiMBgwMzNj5MiRnDlzhitXrhASEoKDgwNt27bF19eXwYMH89vf/hZvb29A5uKD1ngxbWpqKidOnODpp5+md+/euLm5sXHjRq5evcr333/P6dOn6d+/P05OTiYedcslwbgQv6LG22tHRkbi6emJj48P3bt3x8PDg+XLlyvt1bZt24a3tzezZ89GrVZLpqeZ5ObmEhkZycCBAwkKCsLV1RVvb29KSko4dOgQGRkZ7N+/nzNnzhATE4NGo6G2tlYekloIYzavuLiYW7ducePGDWXDnpMnT1JRUaG8hVqwYAE//PADo0aNkoy4CTVu+RoeHk5ycjKZmZlKQC6aV+PFtDNnzuQf//gHRUVF7N27Fzs7O8aNG0d4eDgajYYLFy5w/vx5hgwZgoeHh4lH3nLJpj9C/MpKS0uZMGECY8eOZcKECU2C7AMHDvDuu+9iZ2dHdXU1KSkpsmlMM6qtrWXBggX4+voyZ86cJr8rKSnh5MmTSru72NhYtFqtPCS1IMZrlZWVxSuvvEK7du0oLy+nW7duzJ07lz//+c9kZmaSl5dHx44dlV0D5To/HIzZ2IqKCl5++WXMzc1ZunSp0kpUNK+EhASOHDnCnj17AJg9ezZHjx4lOjqayMhI7O3tAcjJySEgIEDm0H9AlrYK8SsxfhHduHGDtm3bMmHCBOXn0FBH/vTTTxMWFoZer8fd3V2pEZeFYs3DzMwMnU6n9HHX6XTKCn9zc3MGDRrEoEGDlOOlm0bLolKpKC0tZeHChUyaNImxY8dy+fJlhgwZwpAhQ5g+fTqVlZV8/fXXtG/fntDQUOXNh1xn09NoNNTV1WFjY8OmTZuYMmUKVVVVph5Wq1RdXY2NjQ1LliwBYOPGjZw7d44pU6aQmJiIXq/n2WefxcfHh4CAABOPtuWTVJwQ/6G6ujqgIbADsLS05O7du5w+fRpAqU8tLS3l1KlTODs7Kzs6SseG5lFSUkJxcbHy//v37wcaAnC1Wo1arWbz5s3k5uYqxxjrWMXDzzgHoWFhrpWVFWPHjqW+vp5FixYxfPhw+vTpw82bN3F0dOTZZ58lLCxMCf7kOj94ja+RkXGTpcaM18TW1pYPP/yQLl26NMfwxI9YWloSERFBYGAg3377Lampqbz//vtMmTIFd3d3MjIyfvI2V7Li/z4JxoX4Dxiz2tnZ2cTGxnLx4kWcnZ1xdXXlv//7vykqKlKOffPNN0lNTW3yBSalKQ9eVlYWERERfPfdd+h0OmbNmkVZWRmxsbHodDql7V1mZiYdOnRQzpMbS8tgMBjQaDRkZWURFxdHaWkpNjY25OXl8dxzz+Hh4cHq1asB2LZtG+fPn29yvjwMP3iN3/6dPn2ajIwM9Hr9z37/aTQa9Hq9XBsTs7Ozw8rKisuXLxMWFkaHDh1IS0sjKCiI119/HS8vL1MP8ZEh6QAh/gPGXf2ioqKYNm0adnZ22NnZMXPmTN544w2uXbumlKJcunSJjz/+2NRDblUKCwuZNm0aL7zwAiNGjADAz8+PGTNmsG7dOoYOHYqHhwd1dXUkJiYqbyvkIallMF6r8vJy4uPjGTp0KJ06daKmpoZnnnmGZ555hrfffhuA1157jdu3bxMcHGziUbc+xqB6zpw5FBYWYjAY0Gq1REdH87vf/Q4rK6smx9fW1qLVaiktLWXNmjXK+g1hGr6+vqxcuZL6+nr279/P2rVrlVI/8euQBZxC/JsMBoPyGtzDw4NXXnmlye9zc3M5deoUubm5tGvXjvHjx8uufs3siy++4PPPP2fNmjUYDIYmXQKgYQc5GxsbAgICUKvVcm1aoKKiIubPn4+Pjw8rV64EoKysjOjoaDp37oyNjQ0lJSXk5OTIgmkT2rZtG19++SVJSUloNBpGjBiBi4sLGzduxNLSUjnOOAfLysp47rnnWLx4MeHh4SYcuQBIS0vj3Llz9OjRg8cee0wWa/7K5K4jxL9JpVKh0WioqanB398f+NeCQIPBgKurK6NHj25yjtSnNq/q6mqys7MpKSnByclJaXtXWFiIubk5oaGhyrH19fVybVoQYzBQUVGBTqfj888/Z+nSpVhaWtK2bVt27drFZ599RnV1NX5+fsTFxcnDcDP68cL04uJiRo0ahUajYevWrZSWlrJ582ZUKhXl5eXY2tqi1+vRarWUlZUxevRoYmNj6devnwk/hTAaMGCA7Hr6AElqQIj/h8Yvkhr/96effgr8a0GgRqNh48aNXLx4scn5UgP54JWUlJCVlcWNGzfw8/PDxcWFM2fOUF1drQRhq1ev5u9//3uT8yRT2jIYFwIas3L+/v7Ex8fj6enJtGnTqK2tBcDe3p5x48YxefJkxo4di5mZmTwMNyPjd51x4bSVlRW5ubmsX7+epKQkPvjgAzw8PPjoo4/YvXs3dXV1SmnKmDFjeP311yUQf4hJVvzXJWUqQvxCxkxPaWkpNTU11NbW4uXlRV5eHsuWLcPHx4fly5djMBhYuXIlV65cYffu3RLkNaOsrCxmzpyJo6MjhYWFLF68mLS0NAoLC+nduzdeXl6kp6fzww8/kJqaKoFZC2MsL7l06RIpKSm0bdsWT09Phg0bRn5+PkuXLsXMzIzt27dL2dFDIDk5mYSEBA4fPsyxY8dYv349eXl57Ny5k5CQEL799lsWLFhAXFwcTzzxBAaDgdmzZ/OHP/xBSlNEqyLBuBC/gDEIyMrKYv78+ahUKuzt7QkPD2fSpEkcO3aMVatWcevWLTw9Pamvr2fnzp1Sn9qM8vPziYyMJDo6milTppCUlMTOnTuZPHkyxcXFVFRUcO3aNVxcXFi2bBlarVZ6vLcgxnl05coVxowZw6BBg7h37x4XL14kNDSUlStXkp+fT0xMDGVlZXz88ceSvTOxiooKFixYwOjRowkPD2f79u2cOHECAB8fHz799FOWLFnCsGHDlLKj6urqJjXkQrQGEowL8Qvl5eUxceJEpk6dylNPPcWRI0d44403mDt3LpMmTQLg2LFjODo64uvrK5m5ZmQwGEhJSaGgoIB58+ZhMBgYN24cer2e2tpahg8fzrBhw5ps1yzXpuW5desWX3zxBeXl5bz00ktUV1dz+fJlZsyYwahRo5g1axYXLlwgJSWFxYsXy0NwM7pf0kGn0/HWW29RXl7OmjVrgIbWhtnZ2djY2ODm5ka3bt2Ukj95eBKtlSY2NjbW1IMQ4mFnMBj461//Srt27Zg9ezY2NjYsXrwYHx8f0tLSqKurIzQ0lPbt2+Pg4IBKpZINfZqRSqWiffv2uLq64uLiQlRUFO7u7uzYsYP09HQ+/fRTAgMDCQoKAv7Vm1q0HAaDgT/+8Y8kJSXh5eVF//79MTc3x9nZGXt7e9LS0hg0aBBubm70799fmYMS4DUP479zfHy88jNHR0eCgoJISEjA3t6ekJAQXF1dCQkJISAgADc3N+VcuU6iNZO0gRC/gEqlYvDgwUyaNIn6+nqioqIIDAwkMTGR9u3bs2rVKpKTk5ucI1m55uXg4EBISAg5OTm0adOGtWvXAuDh4cGLL77I008/rRwrN/6WR6VSsWbNGvr160dubi65ubnKDo5+fn6Ul5ej0+maXFuZgw/ej3fWLC8v56OPPuLVV1/l448/xtbWlhdffJGrV69SU1Nz3504hWjt5B2tEL+Qq6srKpWKY8eOoVarlQyQv78/kyZNYuDAgSYeoYCGLg7ffPMNGzduJCcnh2vXrrFnzx5l8yXJiLdMtbW12NjYsHr1aqZNm8batWsZMWIEffr0Yc+ePbRp04a2bduaepitSuP5lJqaSlVVFdHR0RgMBs6dO8e7777LqVOnuHjxIiUlJYwYMUJpAyuE+BcpUxHin4w9qH+OMeN2/fp1vv76a/R6PTt37iQ3N5cFCxag0Wj+z78hHjwHBwc8PDz46quvsLCw4L333sPMzEzKhlo448OUpaUlgwcP5pNPPmHHjh3cunWLqqoqNm/erOygKm8+HrzGG2jNnDmTf/zjHxQVFbF3717s7OwYN24c4eHhaDQaLly4wPnz5xkyZEiTdRtCiAaygFMIICMjg8LCQgYNGoSNjc3/emx5eTl/+tOfqKys5O7du2zZskW6pjyEdDod5ubmgCzWfJQYs7FVVVXMnDkTa2tr/vjHP+Ln5ye7AppAQkICR44cYc+ePQDMnj2bo0ePEh0dTWRkJPb29gDk5OQQEBAg10iI+5BgXAggLi6OkydPMmHCBH73u99hbW193+OMAbdOpwNAq9WiUqkk2HuIyc2/5fq5B1xjQF5RUcG0adNwdnZm9uzZBAYGmmCUrVd1dTW7d++mV69edO3alY0bN7Jv3z4iIyNJTEwkIiKCZ599Fh8fH+UcmY9C/JSk8YQAFi5cSO/evUlOTubQoUNUVlbe9zi1Wo3BYMDMzAxzc3PlpiLlDw8vufG3DMaFfVVVVdTU1AA/vwBTo9Gg0+mwsbHh/fff5/Lly7z33nvo9fpmG68AS0tLIiIiCAwM5NtvvyU1NZX333+fKVOm4O7uTkZGxk+uocxHIX5KUnmi1TNm2ebNm8eaNWvYt28fAAMHDvzZDLnxBnPy5EkCAgKwtbVttvEK8agx1vNfuHCBmJgYbGxsuHfvHvPmzSMkJOQn89BgMCglSNnZ2WzevBloeFMlmpednR0Aly9fJiwsjA4dOpCWlkZQUBDR0dF4eXmZeIRCPPwkMy5atR/3m3711VcJCQlh3759982QN37F+uc//5mlS5dSVVXVrGMW4lGjVqu5fv06U6ZMYeDAgaxfv55OnTrx6quvcvny5SbHNp6DSUlJREREAMjCQBPz9fUlNTWVhQsXMmPGDPr16ydlQ0L8QhKMi1arrq4OlUpFcXExeXl5ZGVlAbBgwYL7BuSNuzQkJSWxYcMG4uPjcXV1NdlnEKKlMy5bunz5Mn379mX69OlYW1tz/vx5Hn/8cTp37qys0TDOWWiYg+vXr2ffvn0SiD8EHn/8cbZs2YKPjw/bt29n8ODByJI0IX4ZWcApWiXjwrCsrCzmzp2LtbU19+7d46mnnmLevHkArFq1ih9++IGhQ4cyfPhwrKysgH8FATt27KBz586m/BhCtFjGDLder0er1XLw4EG2bdvGe++9x0svvYSPjw/vvPMORUVF/OUvfyE6OhoLCwsAPvzwQ9atWydz8CEnizWF+GUkMy5aJbVazZUrV5g+fToTJkwgKSmJJUuWsGXLFt555x0A5s+fj6enJxcuXFAC8b/85S+sXr1aggAhfgW3bt1i9OjRlJaW0rt3bzw9PRk7diwdOnRQ5uGaNWs4ffq0UiP++eefs2LFCnbu3Clz8CEngbgQv4ws4BStjjFbk56ezoABAxg/fjz19fWsX7+e/v37s2vXLjQaDXPmzGHFihXKq9a6ujru3LnDf/3Xf0ktpBD/IZVKhYuLCy4uLqSmpvLiiy/SvXt3CgoKCAoK4vDhwxw4cIDs7GxSUlKUwK5r16589tln+Pn5mfgTCCHEr0N24BSPPGPwbdwdU6/Xo9Fo8PX1JSwsDDMzM1544QXat2/Pu+++S2ZmJsnJydja2tK9e3flXI1GQ7du3XBycjL1RxKiRWq87sL4kHv79m1OnTrF0KFD6dGjBxYWFly/fp3MzEycnJxISEhAq9VSW1uLWq3G1tYWBwcHU34MIYT4VUnNuHikpaWlUVlZyZNPPomVlRUXL14kISEBKysrnJycmDp1KvX19cyZM4e9e/cC8NZbb9GnTx/69+8vG/kI8SvLz8+ntrYWJycn7OzsuH37Nn/4wx+IiIhgxowZ9z3H+DAshBCPIsmMi0fa9evXiY2Nxd/fH61Wy8SJExk4cCDOzs4UFBSwYcMGunbtSnZ2NhcuXCA5OZkzZ86waNEiNBqNko0TQvz7Gi/kS0hIYOvWrZw/fx4LCwuCg4Px8PDgxIkTBAcHY2dn95NaY5mDQohHmWTGxSPv6NGjLFu2jIiICIqLi1m4cCEANTU1vPnmm5w9e5Y+ffpQVlZGWVkZ69evR6vV/uxW3EKIX86Y1S4pKeHOnTs4OzuTk5PD8ePH2bZtG6NHj6auro7s7GymT59Onz59TD1kIYRoVvIOXjzywsLCWLlyJTNmzMDb25vi4mKcnZ0xNzfnueeeIz8/n1GjRhEYGKhk8Gpra6VERYj/kHFnzaysLGbOnEnbtm0pKipi7ty51WwwIgAAAhFJREFUREdH079/f7744gsyMzNJT0/HwcFBgnEhRKsjZSqiVfDw8KBXr1588skn+Pj44Onpibm5Oa6uruzYsYN+/frh5uaGSqX6ya6cQoh/j0qlIj8/n0mTJjF+/Hji4uKwsrJiw4YN6PV6+vXrR79+/Rg4cCAODg689tpr8jZKCNHqSJmKaFUyMjJYtmwZQ4cOpXv37hw6dIhz586RnJwsAbgQvzKDwUBKSgoFBQXMmzcPg8HAuHHjMBgMlJeX88wzzzB8+HC8vLyUc+StlBCitZEUhGhVevXqxVtvvcWuXbtITEzE0dGRvXv3otFoqKurM/XwhHikqFQqBg0axODBgwGIioqiffv27N27l4CAABITE8nMzGxyjgTiQojWRoJx0er07NmTzZs34+zszCuvvKL0MJbMuBC/PgcHB0JCQsjJyaFNmzasXbsWaCgde/HFFxk2bJiJRyiEEKYlZSqi1TIu1mzcdk0I8WDk5eUxdOhQZs+eTU5ODteuXWPPnj2o1WrpIy6EaNUkGBetmgTiQjSf1NRUDhw4gK2tLfHx8dJCVAghkGBcCCFEM9LpdJibmwOyWFMIIUCCcSGEECYgb6WEEKKBvBsUQgjR7CQQF0KIBhKMCyGEEEIIYSISjAshhBBCCGEiEowLIYQQQghhIhKMCyGEEEIIYSISjAshhBBCCGEiEowLIYQQQghhIv8D6PCO/Y5GIo0AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x1080 with 6 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IQ8yj2fCK8yJ"
      },
      "source": [
        "hyper_param: Tuning options are GridSearch ('GS') and RandomizedSearch ('RS'). Default is 'GS'.\n",
        "\n",
        "feature_reduction: Default = 'True' but it can be set to False if you don't want automatic    \n",
        "\n",
        "Boosting Flag: you have 4 possible choices (default is False):                               \n",
        "  None = This will build a Linear Model                                                  \n",
        "  False = This will build a Random Forest or Extra Trees model (also known as Bagging)        \n",
        "  True = This will build an XGBoost model                                                     \n",
        "  CatBoost = THis will build a CatBoost model (provided you have CatBoost installed)          \n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BjcoyRn-KGe4",
        "outputId": "213ea5ed-bcba-401b-d4bc-b3feb3082e4a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        }
      },
      "source": [
        "features"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Contract',\n",
              " 'Online_Security',\n",
              " 'Tech_Support',\n",
              " 'Dependents',\n",
              " 'Internet_Service',\n",
              " 'Tenure_Months',\n",
              " 'Streaming_Movies',\n",
              " 'Phone_Service',\n",
              " 'Streaming_TV',\n",
              " 'Paperless_Billing',\n",
              " 'Online_Backup',\n",
              " 'Payment_Method',\n",
              " 'Total_Charges']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wg-qo8naI9pU"
      },
      "source": [
        "from sklearn.metrics import classification_report, confusion_matrix"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WmKtpTtbKmgL",
        "outputId": "e3a5117b-cae4-46f2-b223-fbad014eaece",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 439
        }
      },
      "source": [
        "testm"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Contract</th>\n",
              "      <th>Online_Security</th>\n",
              "      <th>Tech_Support</th>\n",
              "      <th>Dependents</th>\n",
              "      <th>Internet_Service</th>\n",
              "      <th>Tenure_Months</th>\n",
              "      <th>Streaming_Movies</th>\n",
              "      <th>Phone_Service</th>\n",
              "      <th>Streaming_TV</th>\n",
              "      <th>Paperless_Billing</th>\n",
              "      <th>Online_Backup</th>\n",
              "      <th>Payment_Method</th>\n",
              "      <th>Total_Charges</th>\n",
              "      <th>Churn_Label_predictions</th>\n",
              "      <th>Churn_Label_proba_No</th>\n",
              "      <th>Churn_Label_proba_Yes</th>\n",
              "      <th>Churn_Label_Logistic_Regression_CV_predictions</th>\n",
              "      <th>Churn_Label_Decision_Tree_predictions</th>\n",
              "      <th>Churn_Label_Naive_Bayes_predictions</th>\n",
              "      <th>Churn_Label_Bagging_Classifier_predictions</th>\n",
              "      <th>Churn_Label_XGBoost_predictions</th>\n",
              "      <th>Churn_Label_Ensembled_predictions</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>4930</th>\n",
              "      <td>0.50</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.38</td>\n",
              "      <td>0.50</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.50</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.75</td>\n",
              "      <td>No</td>\n",
              "      <td>0.95</td>\n",
              "      <td>0.05</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4931</th>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.50</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.33</td>\n",
              "      <td>0.95</td>\n",
              "      <td>No</td>\n",
              "      <td>0.95</td>\n",
              "      <td>0.05</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4932</th>\n",
              "      <td>0.00</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.50</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.50</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.50</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.23</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0.34</td>\n",
              "      <td>0.66</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4933</th>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.15</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.33</td>\n",
              "      <td>0.64</td>\n",
              "      <td>No</td>\n",
              "      <td>0.68</td>\n",
              "      <td>0.32</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4934</th>\n",
              "      <td>1.00</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.50</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.54</td>\n",
              "      <td>0.50</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.50</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>No</td>\n",
              "      <td>0.99</td>\n",
              "      <td>0.01</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7038</th>\n",
              "      <td>1.00</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.50</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.50</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.12</td>\n",
              "      <td>No</td>\n",
              "      <td>0.99</td>\n",
              "      <td>0.01</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7039</th>\n",
              "      <td>0.50</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.33</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.24</td>\n",
              "      <td>No</td>\n",
              "      <td>0.95</td>\n",
              "      <td>0.05</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7040</th>\n",
              "      <td>0.50</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.50</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.33</td>\n",
              "      <td>0.87</td>\n",
              "      <td>No</td>\n",
              "      <td>0.97</td>\n",
              "      <td>0.03</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7041</th>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.15</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.67</td>\n",
              "      <td>0.46</td>\n",
              "      <td>No</td>\n",
              "      <td>0.76</td>\n",
              "      <td>0.24</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7042</th>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.92</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.83</td>\n",
              "      <td>No</td>\n",
              "      <td>0.76</td>\n",
              "      <td>0.24</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2113 rows × 22 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      Contract  Online_Security  Tech_Support  Dependents  Internet_Service  \\\n",
              "4930      0.50             0.50          0.50        0.00              1.00   \n",
              "4931      1.00             1.00          1.00        0.00              0.50   \n",
              "4932      0.00             0.50          0.50        0.00              1.00   \n",
              "4933      0.00             0.00          0.00        0.00              0.00   \n",
              "4934      1.00             0.50          0.50        1.00              1.00   \n",
              "...        ...              ...           ...         ...               ...   \n",
              "7038      1.00             0.50          0.50        0.00              1.00   \n",
              "7039      0.50             1.00          1.00        1.00              0.00   \n",
              "7040      0.50             0.00          0.00        1.00              0.50   \n",
              "7041      0.00             1.00          0.00        1.00              0.00   \n",
              "7042      1.00             1.00          1.00        0.00              0.50   \n",
              "\n",
              "      Tenure_Months  Streaming_Movies  Phone_Service  Streaming_TV  \\\n",
              "4930           0.38              0.50           1.00          0.50   \n",
              "4931           1.00              1.00           1.00          1.00   \n",
              "4932           0.01              0.50           1.00          0.50   \n",
              "4933           0.15              0.00           1.00          0.00   \n",
              "4934           0.54              0.50           1.00          0.50   \n",
              "...             ...               ...            ...           ...   \n",
              "7038           1.00              0.50           1.00          0.50   \n",
              "7039           0.33              1.00           1.00          1.00   \n",
              "7040           1.00              1.00           1.00          1.00   \n",
              "7041           0.15              0.00           0.00          0.00   \n",
              "7042           0.92              1.00           1.00          1.00   \n",
              "\n",
              "      Paperless_Billing  Online_Backup  Payment_Method  Total_Charges  \\\n",
              "4930               0.00           0.50            1.00           0.75   \n",
              "4931               1.00           1.00            0.33           0.95   \n",
              "4932               1.00           0.50            1.00           0.23   \n",
              "4933               0.00           0.00            0.33           0.64   \n",
              "4934               0.00           0.50            1.00           1.00   \n",
              "...                 ...            ...             ...            ...   \n",
              "7038               1.00           0.50            0.00           0.12   \n",
              "7039               1.00           0.00            1.00           0.24   \n",
              "7040               1.00           1.00            0.33           0.87   \n",
              "7041               1.00           0.00            0.67           0.46   \n",
              "7042               1.00           0.00            0.00           0.83   \n",
              "\n",
              "     Churn_Label_predictions  Churn_Label_proba_No  Churn_Label_proba_Yes  \\\n",
              "4930                      No                  0.95                   0.05   \n",
              "4931                      No                  0.95                   0.05   \n",
              "4932                     Yes                  0.34                   0.66   \n",
              "4933                      No                  0.68                   0.32   \n",
              "4934                      No                  0.99                   0.01   \n",
              "...                      ...                   ...                    ...   \n",
              "7038                      No                  0.99                   0.01   \n",
              "7039                      No                  0.95                   0.05   \n",
              "7040                      No                  0.97                   0.03   \n",
              "7041                      No                  0.76                   0.24   \n",
              "7042                      No                  0.76                   0.24   \n",
              "\n",
              "     Churn_Label_Logistic_Regression_CV_predictions  \\\n",
              "4930                                             No   \n",
              "4931                                             No   \n",
              "4932                                            Yes   \n",
              "4933                                            Yes   \n",
              "4934                                             No   \n",
              "...                                             ...   \n",
              "7038                                             No   \n",
              "7039                                             No   \n",
              "7040                                             No   \n",
              "7041                                             No   \n",
              "7042                                             No   \n",
              "\n",
              "     Churn_Label_Decision_Tree_predictions  \\\n",
              "4930                                    No   \n",
              "4931                                    No   \n",
              "4932                                    No   \n",
              "4933                                    No   \n",
              "4934                                    No   \n",
              "...                                    ...   \n",
              "7038                                    No   \n",
              "7039                                    No   \n",
              "7040                                    No   \n",
              "7041                                    No   \n",
              "7042                                    No   \n",
              "\n",
              "     Churn_Label_Naive_Bayes_predictions  \\\n",
              "4930                                  No   \n",
              "4931                                  No   \n",
              "4932                                 Yes   \n",
              "4933                                 Yes   \n",
              "4934                                  No   \n",
              "...                                  ...   \n",
              "7038                                  No   \n",
              "7039                                  No   \n",
              "7040                                  No   \n",
              "7041                                  No   \n",
              "7042                                  No   \n",
              "\n",
              "     Churn_Label_Bagging_Classifier_predictions  \\\n",
              "4930                                         No   \n",
              "4931                                         No   \n",
              "4932                                        Yes   \n",
              "4933                                        Yes   \n",
              "4934                                         No   \n",
              "...                                         ...   \n",
              "7038                                         No   \n",
              "7039                                         No   \n",
              "7040                                         No   \n",
              "7041                                         No   \n",
              "7042                                         No   \n",
              "\n",
              "     Churn_Label_XGBoost_predictions Churn_Label_Ensembled_predictions  \n",
              "4930                              No                                No  \n",
              "4931                              No                                No  \n",
              "4932                             Yes                               Yes  \n",
              "4933                              No                                No  \n",
              "4934                              No                                No  \n",
              "...                              ...                               ...  \n",
              "7038                              No                                No  \n",
              "7039                              No                                No  \n",
              "7040                              No                                No  \n",
              "7041                              No                                No  \n",
              "7042                              No                                No  \n",
              "\n",
              "[2113 rows x 22 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QoqsH4LeLD8D",
        "outputId": "7814d261-af68-4500-81a1-5f91fa3b9abe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        " print(confusion_matrix(test_df[target].values,testm['Churn_Label_predictions'].values))"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[1712  401]\n",
            " [   0    0]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z2rkWfPvOWUs",
        "outputId": "a84062da-942c-4da7-9d3c-a969a378041c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        " print(confusion_matrix(test_df[target].values,testm['Churn_Label_Logistic_Regression_CV_predictions'].values))"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[1515  598]\n",
            " [   0    0]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bWEcJG20TbcF",
        "outputId": "4da0f8f9-f381-4950-91cc-dbb9cd07ba6c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        " print(confusion_matrix(test_df[target].values,testm['Churn_Label_Ensembled_predictions'].values))"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[1717  396]\n",
            " [   0    0]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IO2oYoHPLjWc",
        "outputId": "21b14d36-69c8-4e99-d180-a455af5cb8cb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        }
      },
      "source": [
        "print(classification_report(test_df[target].values,testm['Churn_Label_XGBoost_predictions'].values))"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "          No       1.00      0.81      0.90      2113\n",
            "         Yes       0.00      0.00      0.00         0\n",
            "\n",
            "    accuracy                           0.81      2113\n",
            "   macro avg       0.50      0.41      0.45      2113\n",
            "weighted avg       1.00      0.81      0.90      2113\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7TmezM_STsYq",
        "outputId": "2d22227a-41f3-4375-b4ee-6b790dc163b8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        }
      },
      "source": [
        "print(classification_report(test_df[target].values,testm['Churn_Label_Ensembled_predictions'].values))"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "          No       1.00      0.81      0.90      2113\n",
            "         Yes       0.00      0.00      0.00         0\n",
            "\n",
            "    accuracy                           0.81      2113\n",
            "   macro avg       0.50      0.41      0.45      2113\n",
            "weighted avg       1.00      0.81      0.90      2113\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ydtu2LgJT7RP"
      },
      "source": [
        "#References:\n",
        "Link to supporting video for below notebook walkhthrough - https://www.youtube.com/watch?v=RqhTzEVkfBQ\n"
      ]
    }
  ]
}